BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21031@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nMost people take for granted that when they speak\, t hey will be heard and understood. But for the millions who live with speec h impairments caused by physical or neurological conditions\, trying to co mmunicate with others can be difficult and lead to frustration. While ther e have been a great number of recent advances in Automatic Speech Recognit ion (ASR) technologies\, these interfaces can be inaccessible for those wi th speech impairments.\nIn this talk\, we will present Parrotron\, an end- to-end-trained speech-to-speech conversion model that maps an input spectr ogram directly to another spectrogram\, without utilizing any intermediate discrete representation. The system is also trained to emit words in addi tion to a spectrogram\, in parallel. We demonstrate that this model can be trained to normalize speech from any speaker regardless of accent\, pro sody\, and background noise\, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We fu rther show that this normalization model can be adapted to normalize highl y atypical speech from speakers with a variety of speech impairments (due to\, ALS\, Cerebral-Palsy\, Deafness\, Stroke\, Brain Injury\, etc.) \, r esulting in significant improvements in intelligibility and naturalness\, measured via a speech recognizer and listening tests. Finally\, demonstrat ing the utility of this model on other speech tasks\, we show that the sam e model architecture can be trained to perform a speech separation task.\n Dimitri will give a brief description of some key moments in development o f speech recognition algorithms that he was involved in and their applicat ions to YouTube closed captions\, Live Transcribe and wearable subtitles. \nFadi will then speak about the development of Parrotron.\nBiographies\nD imitri Kanevsky started his career at Google working on speech recognition algorithms. Prior to joining Google\, Dimitri was a Research staff member in the Speech Algorithms Department at IBM. Prior to IBM\, he worked at a number of centers for higher mathematics\, including Max Planck Institu te in Germany and the Institute for Advanced Studies in Princeton. He curr ently holds 295 US patents and was Master Inventor at IBM. MIT Technology Review recognized Dimitri conversational biometrics based security patent as one of five most influential patents for 2003. In 2012 Dimitri was hono red at the White House as a Champion of Change for his efforts to advance access to science\, technology\, engineering\, and math.\nFadi Biadsy is a senior staff research scientist at Google NY for the past ten years. He h as been exploring and leading multiple projects at Google\, including spee ch recognition\, speech conversion\, language modeling\, and semantic unde rstanding. He received his PhD from Columbia University in 2011. At Colum bia\, he researched a variety of speech and language processing projects i ncluding\, dialect and accent recognition\, speech recognition\, charismat ic speech and question answering. He holds a BSc and MSc in mathematics a nd computer science. He worked on handwriting recognition during his maste rs degree and he worked as a senior software developer for five years at D alet digital media systems building multimedia broadcasting systems. DTSTART;TZID=America/New_York:20211105T120000 DTEND;TZID=America/New_York:20211105T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fadi Biadsy and Dimitri Kanevsky (Google) “Speech Recognition: From Speaker Dependent to Speaker Independent to Full Personalization” “Parrot ron: A Unified E2E Speech-to Speech Conversion and ASR Model for Atypical Speech” URL:https://www.clsp.jhu.edu/events/fadi-biadsy-and-dimitri-kanevsky-google / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nMost people take for granted that when they speak\, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological condi tions\, trying to communicate with others can be difficult and lead to fru stration. While there have been a great number of recent advances in Autom atic Speech Recognition (ASR) technologies\, these interfaces can be inacc essible for those with speech impairments.
\nIn this talk\, we will present Parrotron\, an end-to-end-trained speech-to-sp eech conversion model that maps an input spectrogram directly to another s pectrogram\, without utilizing any intermediate discrete representation. T he system is also trained to emit words in addition to a spectrogram\, in parallel. We demonstrate that this model can be trained to normalize spe ech from any speaker regardless of accent\, prosody\, and background noise \, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We further show that this normal ization model can be adapted to normalize highly atypical speech from spea kers with a variety of speech impairments (due to\, ALS\, Cerebral-Palsy\, Deafness\, Stroke\, Brain Injury\, etc.) \, resulting in significant imp rovements in intelligibility and naturalness\, measured via a speech recog nizer and listening tests. Finally\, demonstrating the utility of this mod el on other speech tasks\, we show that the same model architecture can be trained to perform a speech separation task.
\nDimitri will give a brief description of some key moments in development o f speech recognition algorithms that he was involved in and their applicat ions to YouTube closed captions\, Live Transcribe and wearable subtitles.
\nFadi will then speak about the development of Parrotron.
\nBiographies
\nDimitri K anevsky started his career at Google working on speech recognitio n algorithms. Prior to joining Google\, Dimitri was a Research staff membe r in the Speech Algorithms Department at IBM. Prior to IBM\, he worked a t a number of centers for higher mathematics\, including Max Planck Instit ute in Germany and the Institute for Advanced Studies in Princeton. He cur rently holds 295 US patents and was Master Inventor at IBM. MIT Technology Review recognized Dimitri conversational biometrics based security patent as one of five most influential patents for 2003. In 2012 Dimitri was hon ored at the White House as a Champion of Change for his efforts to advance access to science\, technology\, engineering\, and math.
\nFadi Biadsy is a senior staff research scientist at Google NY for the past ten years. He has been exploring and leading multiple projects a t Google\, including speech recognition\, speech conversion\, language mod eling\, and semantic understanding. He received his PhD from Columbia Uni versity in 2011. At Columbia\, he researched a variety of speech and langu age processing projects including\, dialect and accent recognition\, speec h recognition\, charismatic speech and question answering. He holds a BSc and MSc in mathematics and computer science. He worked on handwriting rec ognition during his masters degree and he worked as a senior software deve loper for five years at Dalet digital media systems building multimedia br oadcasting systems.
\n X-TAGS;LANGUAGE=en-US:2021\,Biadsy and Kanevsky\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-21041@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?\nBiography\nAndrew Piper is Professor and William Dawson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n_\,\n a l aboratory for cultural analytics\, and editor of the /Journal of Cultural Analytics/\, an open-access journal dedicated to the computational study o f culture. He is the author of numerous books and articles on the relation ship of technology and reading\, including /Book Was There: Reading in Ele ctronic Times/(Chicago 2012)\, /Enumerations: Data and Literary Study/(Chi cago 2018)\, and most recently\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data/(Cambridge 2020). DTSTART;TZID=America/New_York:20211112T120000 DTEND;TZID=America/New_York:20211112T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Andrew Piper (McGill University) ” How can we use machine learning to understand narration?” URL:https://www.clsp.jhu.edu/events/andrew-piper-mcgill-university-how-can- we-use-machine-learning-to-understand-narration/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?
\nBiography
\n< p>Andrew Piper is Professor and William D awson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n\na laboratory for cultural ana lytics\, and editor of the /Journal of Cultural Analytics/\, an open-acces s journal dedicated to the computational study of culture. He is the autho r of numerous books and articles on the relationship of technology and rea ding\, including /Book Was There: Reading in Electronic Times/(Chicago 201 2)\, /Enumerations: Data and Literary Study/(Chicago 2018)\, and most rece ntly\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data /(Cambridge 2020).
\n X-TAGS;LANGUAGE=en-US:2021\,November\,Piper END:VEVENT BEGIN:VEVENT UID:ai1ec-21057@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab. \nBiography\nHeidi is a Senior Lecturer (associate professor) in Computer Science at the University of Sheffield\, United Kingdom. Her research inte rests are on the application of AI-based voice technologies to healthcare. In particular\, the detection and monitoring of people’s physical and men tal health including verbal and non-verbal traits for expressions of emoti on\, anxiety\, depression and neurodegenerative conditions in e.g.\, thera peutic or diagnostic settings. DTSTART;TZID=America/New_York:20211119T120000 DTEND;TZID=America/New_York:20211119T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Heidi Christensen (University of Sheffield\, UK) Virtual Seminar “A utomated Processing of Pathological Speech: Recent Work and Ongoing Challe nges” URL:https://www.clsp.jhu.edu/events/heidi-christensen-university-of-sheffie ld-uk-virtual-seminar-automated-processing-of-pathological-speech-recent-w ork-and-ongoing-challenges/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab.
\nBiography
\nHeidi is a Senior Lecturer (as sociate professor) in Computer Science at the University of Sheffield\, Un ited Kingdom. Her research interests are on the application of AI-based vo ice technologies to healthcare. In particular\, the detection and monitori ng of people’s physical and mental health including verbal and non-verbal traits for expressions of emotion\, anxiety\, depression and neurodegenera tive conditions in e.g.\, therapeutic or diagnostic settings.
\n X-TAGS;LANGUAGE=en-US:2021\,Christensen\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-21267@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn this talk\, I present a multipronged strategy for zero-shot cross-lingual Information Extraction\, that is the construction of an IE model for some target language\, given existing annotations exclu sively in some other language. This work is part of the JHU team’s effort under the IARPA BETTER program. I explore data augmentation techniques inc luding data projection and self-training\, and how different pretrained en coders impact them. We find through extensive experiments and extension of techniques that a combination of approaches\, both new and old\, leads to better performance than any one cross-lingual strategy in particular.\nBi ography\nMahsa Yarmohammadi is an assistant research scientist in CLSP\, J HU\, who leads state-of-the-art research in cross-lingual language and spe ech applications and algorithms. A primary focus of Yarmohammadi’s researc h is using deep learning techniques to transfer existing resources into ot her languages and to learn representations of language from multilingual d ata. She also works in automatic speech recognition and speech translation . Yarmohammadi received her PhD in computer science and engineering from O regon Health & Science University (2016). She joined CLSP as a post-doctor al fellow in 2017. DTSTART;TZID=America/New_York:20220204T120000 DTEND;TZID=America/New_York:20220204T131500 LOCATION:Ames 234 Presented Virtually via Zoom https://wse.zoom.us/j/967351 83473 SEQUENCE:0 SUMMARY:Mahsa Yarmohammadi (Johns Hopkins University) “Data Augmentation fo r Zero-shot Cross-Lingual Information Extraction” URL:https://www.clsp.jhu.edu/events/mahsa-yarmohammadi-johns-hopkins-univer sity-data-augmentation-for-zero-shot-cross-lingual-information-extraction/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nIn this talk\, I present a multipronged strategy for zero-shot cross-lingual Information Extraction\, that is the construction of an IE model for some target language\, given existing annotations exclu sively in some other language. This work is part of the JHU team’s effort under the IARPA BETTER program. I explore data augmentation techniques inc luding data projection and self-training\, and how different pretrained en coders impact them. We find through extensive experiments and extension of techniques that a combination of approaches\, both new and old\, leads to better performance than any one cross-lingual strategy in particular.
\nBiography
\nAbstr act
\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have questioned the robustness of longit udinal analyses based on statistical methods due to issues of temporal bia s and semantic shift. To what extent are changes in semantics over time af fecting the reliability of longitudinal analyses? We examine this question through a case study: understanding shifts in mental health during the co urse of the COVID-19 pandemic. We demonstrate that a recently-introduced m ethod for measuring semantic shift may be used to proactively identify fai lure points of language-based models and improve predictive generalization over time. Ultimately\, we find that these analyses are critical to produ cing accurate longitudinal studies of social media.
\n X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-21277@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs humans\, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn throug h perception and interaction. We use this understanding to reason beyond w hat we literally observe or read\, imagining how situations might unfold i n the world. Machines today struggle at this kind of reasoning\, which lim its how they can communicate with humans.In my talk\, I will discuss three lines of work to bridge this gap between machines and humans. I will firs t discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks\, using machines in the lo op to filter out spurious biases. Next\, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the wor ld through simulation\, using this knowledge to ground language. From an E nglish-language description of an event\, PIGLeT can anticipate how the wo rld state might change – outperforming text-only models that are orders of magnitude larger. Finally\, I will introduce MERLOT\, which learns about situations in the world by watching millions of YouTube videos with transc ribed speech. Through training objectives inspired by the developmental ps ychology idea of multimodal reentry\, MERLOT learns to fuse language\, vis ion\, and sound together into powerful representations.Together\, these di rections suggest a path forward for building machines that learn language rooted in the world.\nBiography\nRowan Zellers is a final year PhD candida te at the University of Washington in Computer Science & Engineering\, adv ised by Yejin Choi and Ali Farhadi. His research focuses on enabling machi nes to understand language\, vision\, sound\, and the world beyond these m odalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 outstanding paper award. His work has appeared in several me dia outlets\, including Wired\, the Washington Post\, and the New York Tim es. In the past\, he graduated from Harvey Mudd College with a B.S. in Com puter Science & Mathematics\, and has interned at the Allen Institute for AI. DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAs humans\, our understanding of language is grounded
in a rich mental model about “how the world works” – that we learn throug
h perception and interaction. We use this understanding to reason beyond w
hat we literally observe or read\, imagining how situations might unfold i
n the world. Machines today struggle at this kind of reasoning\, which lim
its how they can communicate with humans.
In my talk\, I will discuss three lines of work to bridge
this gap between machines and humans. I will first discuss how we might m
easure grounded understanding. I will introduce a suite of approaches for
constructing benchmarks\, using machines in the loop to filter out spuriou
s biases. Next\, I will introduce PIGLeT: a model that learns physical com
monsense understanding by interacting with the world through simulation\,
using this knowledge to ground language. From an English-language descript
ion of an event\, PIGLeT can anticipate how the world state might change –
outperforming text-only models that are orders of magnitude larger. Final
ly\, I will introduce MERLOT\, which learns about situations in the world
by watching millions of YouTube videos with transcribed speech. Through tr
aining objectives inspired by the developmental psychology idea of multimo
dal reentry\, MERLOT learns to fuse language\, vision\, and sound together
into powerful representations.
Together\, these directions suggest a path forward for building mac
hines that learn language rooted in the world.
Biography strong>
\nRowan Zellers is a final year PhD candidate at the Univers ity of Washington in Computer Science & Engineering\, advised by Yejin Cho i and Ali Farhadi. His research focuses on enabling machines to understand language\, vision\, sound\, and the world beyond these modalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 out standing paper award. His work has appeared in several media outlets\, inc luding Wired\, the Washington Post\, and the New York Times. In the past\, he graduated from Harvey Mudd College with a B.S. in Computer Science & M athematics\, and has interned at the Allen Institute for AI.
\n< /HTML> X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT BEGIN:VEVENT UID:ai1ec-21280@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs AI-driven language interfaces (such as chat-bots) become more integrated into our lives\, they need to become more versatile and reliable in their communication with human users. How can we make pro gress toward building more “general” models that are capable of understand ing a broader spectrum of language commands\, given practical constraints such as the limited availability of labeled data?\nIn this talk\, I will d escribe my research toward addressing this question along two dimensions o f generality. First I will discuss progress in “breadth” — models that add ress a wider variety of tasks and abilities\, drawing inspiration from exi sting statistical learning techniques such as multi-task learning. In part icular\, I will showcase a system that works well on several QA benchmarks \, resulting in state-of-the-art results on 10 benchmarks. Furthermore\, I will show its extension to tasks beyond QA (such as text generation or cl assification) that can be “defined” via natural language. In the second p art\, I will focus on progress in “depth” — models that can handle complex inputs such as compositional questions. I will introduce Text Modular Net works\, a general framework that casts problem-solving as natural language communication among simpler “modules.” Applying this framework to composi tional questions by leveraging discrete optimization and existing non-comp ositional closed-box QA models results in a model with strong empirical pe rformance on multiple complex QA benchmarks while providing human-readable reasoning.\nI will conclude with future research directions toward broade r NLP systems by addressing the limitations of the presented ideas and oth er missing elements needed to move toward more general-purpose interactive language understanding systems.\nBiography\nDaniel Khashabi is a postdoct oral researcher at the Allen Institute for Artificial Intelligence (AI2)\, Seattle. Previously\, he completed his Ph.D. in Computer and Information Sciences at the University of Pennsylvania in 2019. His interests lie at t he intersection of artificial intelligence and natural language processing \, with a vision toward more general systems through unified algorithms an d theories. DTSTART;TZID=America/New_York:20220218T120000 DTEND;TZID=America/New_York:20220218T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Allen Institute for Artificial Intelligence) “The Quest Toward Generality in Natural Language Understanding” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-allen-institute-for-art ificial-intelligence/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAs AI-driven language interfaces (such as c hat-bots) become more integrated into our lives\, they need to become more versatile and reliable in their communication with human users. How can w e make progress toward building more “general” models that are capable of understanding a broader spectrum of language commands\, given practical co nstraints such as the limited availability of labeled data?
\nIn this talk\, I will describe my research toward addressing this ques tion along two dimensions of generality. First I will discuss progress in “breadth” — models that address a wider variety of tasks and abilities\, d rawing inspiration from existing statistical learning techniques such as m ulti-task learning. In particular\, I will showcase a system that works we ll on several QA benchmarks\, resulting in state-of-the-art results on 10 benchmarks. Furthermore\, I will show its extension to tasks beyond QA (su ch as text generation or classification) that can be “defined” via natural language. In the second part\, I will focus on progress in “depth” — mod els that can handle complex inputs such as compositional questions. I will introduce Text Modular Networks\, a general framework that casts problem- solving as natural language communication among simpler “modules.” Applyin g this framework to compositional questions by leveraging discrete optimiz ation and existing non-compositional closed-box QA models results in a mod el with strong empirical performance on multiple complex QA benchmarks whi le providing human-readable reasoning.
\nI will conclude w ith future research directions toward broader NLP systems by addressing th e limitations of the presented ideas and other missing elements needed to move toward more general-purpose interactive language understanding system s.
\nBiography
\nDaniel Khashabi is a postdoctoral researcher at the Allen Institute for Artificia l Intelligence (AI2)\, Seattle. Previously\, he completed his Ph.D. in Com puter and Information Sciences at the University of Pennsylvania in 2019. His interests lie at the intersection of artificial intelligence and natur al language processing\, with a vision toward more general systems through unified algorithms and theories.
\n X-TAGS;LANGUAGE=en-US:2022\,February\,Khashabi END:VEVENT BEGIN:VEVENT UID:ai1ec-21487@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEnormous amounts of ever-changing knowledge are avai lable online in diverse textual styles and diverse formats. Recent advance s in deep learning algorithms and large-scale datasets are spurring progre ss in many Natural Language Processing (NLP) tasks\, including question an swering. Nevertheless\, these models cannot scale up when task-annotated t raining data are scarce. This talk presents my lab’s work toward building general-purpose models in NLP and how to systematically evaluate them. Fir st\, I present a general model for two known tasks of question answering i n English and multiple languages that are robust to small domain shifts. Then\, I show a meta-training approach that can solve a variety of NLP tas ks with only using a few examples and introduce a benchmark to evaluate cr oss-task generalization. Finally\, I discuss neuro-symbolic approaches to address more complex tasks by eliciting knowledge from structured data and language models.\n\nBiography\n\nHanna Hajishirzi is an Assistant Profess or in the Paul G. Allen School of Computer Science & Engineering at the Un iversity of Washington and a Senior Research Manager at the Allen Institut e for AI. Her research spans different areas in NLP and AI\, focusing on d eveloping general-purpose machine learning algorithms that can solve many NLP tasks. Applications for these algorithms include question answering\, representation learning\, green AI\, knowledge extraction\, and conversati onal dialogue. Honors include the NSF CAREER Award\, Sloan Fellowship\, Al len Distinguished Investigator Award\, Intel rising star award\, best pape r and honorable mention awards\, and several industry research faculty awa rds. Hanna received her PhD from University of Illinois and spent a year a s a postdoc at Disney Research and CMU. DTSTART;TZID=America/New_York:20220225T120000 DTEND;TZID=America/New_York:20220225T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 SEQUENCE:0 SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI ) “Toward Robust\, Knowledge-Rich NLP” URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAbstr act
\nSince it is increasingly harder to opt out from inter acting with AI technology\, people demand that AI is capable of maintainin g contracts such that it supports agency and oversight of people who are r equired to use it or who are affected by it. To help those people create a mental model about how to interact with AI systems\, I extend the underly ing models to self-explain—predict the label/answer and explain this predi ction. In this talk\, I will present how to generate (1) free-text explana tions given in plain English that immediately tell users the gist of the r easoning\, and (2) contrastive explanations that help users understand how they could change the text to get another label.
\nBiograph y
\nAna Marasović is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at University of Washington. Her research interests broadly l ie in the fields of natural language processing\, explainable AI\, and vis ion-and-language learning. Her projects are motivated by a unified goal: i mprove interaction and control of the NLP systems to help people make thes e systems do what they want with the confidence that they’re getting exact ly what they need. Prior to joining AI2\, Ana obtained her PhD from Heidel berg University.
\nHow to pronounce my name: the first name i s Ana like in Spanish\, i.e.\, with a long “a” like in “water”\; regarding the last name: “mara” as in actress mara wilson + “so” + “veetch”.
\n< /BODY> X-TAGS;LANGUAGE=en-US:2022\,February\,Marasovic END:VEVENT BEGIN:VEVENT UID:ai1ec-22395@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google\, and Amazon\, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial board of Transactions of the ACL. DTSTART;TZID=America/New_York:20221017T120000 DTEND;TZID=America/New_York:20221017T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
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\nVoice conversion (VC) is a significant aspect of arti ficial intelligence. It is the study of how to convert one’s voice to soun d like that of another without changing the linguistic content. Voice conv ersion belongs to a general technical field of speech synthesis\, which co nverts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice conversion involves multiple speech processing techniques\, such as speech analysis\, spectral convers ion\, prosody conversion\, speaker characterization\, and vocoding. With t he recent advances in theory and practice\, we are now able to produce hum an-like voice quality with high speaker similarity. In this talk\, Dr. Sis man will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the av ailable resources for expressive voice conversion research.
\nDr. Berrak Sisman (Member\, IEEE) received th e Ph.D. degree in electrical and computer engineering from National Univer sity of Singapore in 2020\, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working a s a tenure-track Assistant Professor at the Erik Jonsson School Department of Electrical and Computer Engineering at University of Texas at Dallas\, United States. Prior to joining UT Dallas\, she was a faculty member at S ingapore University of Technology and Design (2020-2022). She was a Postdo ctoral Research Fellow at the National University of Singapore (2019-2020) . She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centre for Speech Technology Research (CSTR)\, U niversity of Edinburgh (2019). She was a visiting researcher at RIKEN Adva nced Intelligence Project in Japan (2018). Her research is focused on mach ine learning\, signal processing\, emotion\, speech synthesis and voice co nversion.
\nDr. Sisman has served as the Area Chair at INTERSPEECH 2 021\, INTERSPEECH 2022\, IEEE SLT 2022 and as the Publication Chair at ICA SSP 2022. She has been elected as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to December 2024. She plays leadership roles i n conference organizations and active in technical committees. She has ser ved as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Association (ISCA).
\n X-TAGS;LANGUAGE=en-US:2022\,November\,Sisman END:VEVENT BEGIN:VEVENT UID:ai1ec-22408@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAI-powered applications increasingly adopt Deep Neura l Networks (DNNs) for solving many prediction tasks\, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased co mputation\, energy\, and storage costs. An effective approach to address t he problem is multi-task learning (MTL) where a set of tasks are learned j ointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly redu ced inference costs and improved generalization performance in many machin e learning applications. In this talk\, we will introduce our recent effor ts on leveraging MTL to improve accuracy and efficiency for edge computing . The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low infer ence costs and high task accuracy.\n\nBiography\n\n\nHui Guan is an Assist ant Professor in the College of Information and Computer Sciences (CICS) a t the University of Massachusetts Amherst\, the flagship campus of the UMa ss system. She received her Ph.D. in Electrical Engineering from North Car olina State University in 2020. Her research lies in the intersection betw een machine learning and systems\, with an emphasis on improving the speed \, scalability\, and reliability of machine learning through innovations i n algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning. DTSTART;TZID=America/New_York:20221111T120000 DTEND;TZID=America/New_York:20221111T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hui Guan (University of Massachusetts Amherst) “Towards Accurate an d Efficient Edge Computing Via Multi-Task Learning” URL:https://www.clsp.jhu.edu/events/hui-guan-university-of-massachusetts-am herst/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAbstr act
\nDriven by the goal of eradicating language barriers o n a global scale\, machine translation has solidified itself as a key focu s of artificial intelligence research today. However\, such efforts have c oalesced around a small subset of languages\, leaving behind the vast majo rity of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe\, high-quality results\, all while ke eping ethical considerations in mind? In this talk\, I introduce No Langua ge Left Behind\, an initiative to break language barriers for low-resource languages. In No Language Left Behind\, we took on the low-resource langu age translation challenge by first contextualizing the need for translatio n support through exploratory interviews with native speakers. Then\, we c reated datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and tr aining improvements to counteract overfitting while training on thousands of tasks. Critically\, we evaluated the performance of over 40\,000 differ ent translation directions using a human-translated benchmark\, Flores-200 \, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achiev es an improvement of 44% BLEU relative to the previous state-of-the-art\, laying important groundwork towards realizing a universal translation syst em in an open-source manner.
\nBiography
\nAngela is a research scientist at Meta AI Research in Ne w York\, focusing on supporting efforts in speech and language research. R ecent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-be hind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation -hokkien/). Before translation\, Angela previously focused on research in on-device models for NLP and computer vision and text generation.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-23304@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.\nBiography\nAlexander “Sasha” Rush is an Asso ciate Professor at Cornell Tech. His work is at the intersection of natura l language processing and generative modeling with applications in text ge neration\, efficient inference\, and controllability. He has written sever al popular open-source software projects supporting NLP research and data science\, and works part-time as a researcher at Hugging Face. He is the s ecretary of ICLR and developed software used to run virtual conferences du ring COVID. His work has received paper and demo awards at major NLP\, vis ualization\, and hardware conferences\, an NSF Career Award\, and a Sloan Fellowship. He tweets and blogs\, mostly about coding and ML\, at @srush_n lp. DTSTART;TZID=America/New_York:20230203T120000 DTEND;TZID=America/New_York:20230203T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sasha Rush (Cornell University) “Pretraining Without Attention” URL:https://www.clsp.jhu.edu/events/sasha-rush-cornell-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.
\nBiography
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\nWhile large language models have advanced the state-o f-the-art in natural language processing\, these models are trained on lar ge-scale datasets\, which may include harmful information. Studies have sh own that as a result\, the models exhibit social biases and generate misin formation after training. In this talk\, I will discuss my work on analyzi ng and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I will first describe my researc h in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part investigates the trus tworthiness of models through the memorization and subsequent generation o f conspiracy theories. I will end my talk with recent work in AI safety re garding text that may lead to physical harm.
\nBiography
\nSharon is a 5th-year Ph.D. candidate at the University of Ca lifornia\, Santa Barbara\, where she is advised by Professor William Wang. Her research interests lie in natural language processing\, with a focus on Responsible AI. Sharon’s research spans the subareas of fairness\, trus tworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LR EC. She has spent summers interning at AWS\, Meta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI.
\n X-TAGS;LANGUAGE=en-US:2023\,February\,Levy END:VEVENT BEGIN:VEVENT UID:ai1ec-23308@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nBiases in datasets\, or unintentionally introduced sp urious cues\, are a common source of misspecification in machine learning. Performant models trained on such data can gender stereotype or be brittl e under distribution shift. In this talk\, we present several results in multimodal and question answering applications studying sources of dataset bias\, and several mitigation methods. We propose approaches where known dimensions of dataset bias are explicitly factored out of a model during learning\, without needing to modify data. Finally\, we ask whether datase t biases can be attributable to annotator behavior during annotation. Draw ing inspiration from work in psychology on cognitive biases\, we show cert ain behavioral patterns are highly indicative of the creation of problemat ic (but valid) data instances in question answering. We give evidence that many existing observations around how dataset bias propagates to models c an be attributed to data samples created by annotators we identify.\nBiogr aphy\nMark Yatskar is an Assistant Professor at University of Pennsylvania in the department of Computer and Information Science. He did his PhD at University of Washington co-advised by Luke Zettlemoyer and Ali Farhadi. H e was a Young Investigator at the Allen Institute for Artificial Intellige nce for several years working with their computer vision team\, Prior. His work spans Natural Language Processing\, Computer Vision\, and Fairness i n Machine Learning. He received a Best Paper Award at EMNLP for work on ge nder bias amplification\, and his work has been featured in Wired and the New York Times. DTSTART;TZID=America/New_York:20230210T120000 DTEND;TZID=America/New_York:20230210T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Yatskar (University of Pennsylvania) “Understanding Dataset Bi ases: Behavioral Indicators During Annotation and Contrastive Mitigations” URL:https://www.clsp.jhu.edu/events/mark-yatskar-university-of-pennsylvania / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nBiases in datasets\, or unintentionally introduced sp urious cues\, are a common source of misspecification in machine learning. Performant models trained on such data can gender stereotype or be brittl e under distribution shift. In this talk\, we present several results in multimodal and question answering applications studying sources of dataset bias\, and several mitigation methods. We propose approaches where known dimensions of dataset bias are explicitly factored out of a model during learning\, without needing to modify data. Finally\, we ask whether datase t biases can be attributable to annotator behavior during annotation. Draw ing inspiration from work in psychology on cognitive biases\, we show cert ain behavioral patterns are highly indicative of the creation of problemat ic (but valid) data instances in question answering. We give evidence that many existing observations around how dataset bias propagates to models c an be attributed to data samples created by annotators we identify.
\n< p>Biography\nMark Yatskar is an Assistan t Professor at University of Pennsylvania in the department of Computer an d Information Science. He did his PhD at University of Washington co-advis ed by Luke Zettlemoyer and Ali Farhadi. He was a Young Investigator at the Allen Institute for Artificial Intelligence for several years working wit h their computer vision team\, Prior. His work spans Natural Language Proc essing\, Computer Vision\, and Fairness in Machine Learning. He received a Best Paper Award at EMNLP for work on gender bias amplification\, and his work has been featured in Wired and the New York Times.
\n\n X-TAGS;LANGUAGE=en-US:2023\,February\,Yatskar END:VEVENT BEGIN:VEVENT UID:ai1ec-23314@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile GPT models have shown impressive performance on summarization and open-ended text generation\, it’s important to assess t heir abilities on more constrained text generation tasks that require sign ificant and diverse rewritings. In this talk\, I will discuss the challeng es of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) paraphrase generation and (ii) text simplifi cation. To address these challenges\, we introduce an interactive Rank-and -Rate evaluation framework. Our results show that GPT-3.5 has made a major step up from fine-tuned T5 in paraphrase generation\, but still lacks the diversity and creativity of humans who spontaneously produce large quanti ties of paraphrases.\nAdditionally\, we demonstrate that GPT-3.5 performs similarly to a single human in text simplification\, which makes it diffic ult for existing automatic evaluation metrics to distinguish between the t wo. To overcome this shortcoming\, we propose LENS\, a learnable evaluatio n metric that outperforms SARI\, BERTScore\, and other existing methods in both automatic evaluation and minimal risk decoding for text generation. \nBiography\nWei Xu is an assistant professor in the School of Interactive Computing at the Georgia Institute of Technology\, where she is also affi liated with the new NSF AI CARING Institute and Machine Learning Center. S he received her Ph.D. in Computer Science from New York University and her B.S. and M.S. from Tsinghua University. Xu’s research interests are in na tural language processing\, machine learning\, and social media\, with a f ocus on text generation\, stylistics\, robustness and controllability of m achine learning models\, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award\, CrowdFlower AI for Everyone Awar d\, Criteo Faculty Research Award\, and Best Paper Award at COLING’18. She has also received funds from DARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/NLP conferences. DTSTART;TZID=America/New_York:20230224T120000 DTEND;TZID=America/New_York:20230224T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei Xu (Georgia Tech) “GPT-3 vs Humans: Rethinking Evaluation of Na tural Language Generation” URL:https://www.clsp.jhu.edu/events/wei-xu-georgia-tech/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nWhile GPT mo dels have shown impressive performance on summarization and open-ended tex t generation\, it’s important to assess their abilities on more constraine d text generation tasks that require significant and diverse rewritings. I n this talk\, I will discuss the challenges of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) par aphrase generation and (ii) text simplification. To address these challeng es\, we introduce an interactive Rank-and-Rate evaluation framework. Our r esults show that GPT-3.5 has made a major step up from fine-tuned T5 in pa raphrase generation\, but still lacks the diversity and creativity of huma ns who spontaneously produce large quantities of paraphrases.
\nAdditionally\, we demon strate that GPT-3.5 performs similarly to a single human in text simplific ation\, which makes it difficult for existing automatic evaluation metrics to distinguish between the two. To overcome this shortcoming\, we propose LENS\, a learnable evaluation metric that outperforms SARI\, BERTScore\, and other existing methods in both automatic evaluation and minimal risk d ecoding for text generation.
\nBiography
\nWei Xu is an assis tant professor in the School of Interactive Computing at the Georgia Insti tute of Technology\, where she is also affiliated with the new NSF AI CARI NG Institute and Machine Learning Center. She received her Ph.D. in Comput er Science from New York University and her B.S. and M.S. from Tsinghua Un iversity. Xu’s research interests are in natural language processing\, mac hine learning\, and social media\, with a focus on text generation\, styli stics\, robustness and controllability of machine learning models\, and re ading and writing assistive technology. She is a recipient of the NSF CARE ER Award\, CrowdFlower AI for Everyone Award\, Criteo Faculty Research Awa rd\, and Best Paper Award at COLING’18. She has also received funds from D ARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/NLP conferences.
\n X-TAGS;LANGUAGE=en-US:2023\,February\,Xu END:VEVENT BEGIN:VEVENT UID:ai1ec-23316@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nUnderstanding the implications underlying a text is c ritical to assessing its impact\, in particular the social dynamics that m ay result from a reading of the text. This requires endowing artificial in telligence (AI) systems with pragmatic reasoning\, for example to correctl y conclude that the statement “Epidemics and cases of disease in the 21st century are “staged”” relates to unfounded conspiracy theories. In this ta lk\, I discuss how shortcomings in the ability of current AI systems to re ason about pragmatics present challenges to equitable detection of false o r harmful language. I demonstrate how these shortcomings can be addressed by imposing human-interpretable structure on deep learning architectures u sing insights from linguistics.In the first part of the talk\, I describe how adversarial text generation algorithms can be used to improve robustne ss of content moderation systems. I then introduce a pragmatic formalism f or reasoning about harmful implications conveyed by social media text. I s how how this pragmatic approach can be combined with generative neural lan guage models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents.\n \nBiogr aphy\nSaadia Gabriel is a PhD candidate in the Paul G. Allen School of Com puter Science & Engineering at the University of Washington\, advised by P rof. Yejin Choi and Prof. Franziska Roesner. Her researchrevolves around n atural language processing and machine learning\, with a particular focus on building systems for understanding how social commonsense manifests in text (i.e. how do people typically behave in social scenarios)\, as well a s mitigating spread of false or harmful text (e.g. Covid-19 misinformation ). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nominatio n\, a 2019 IROS RoboCup best paper nomination and won a best paper award a t the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cu m laude from Mount Holyoke College in Computer Science and Mathematics.\n DTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nUnderstanding the implications underlying a text is c
ritical to assessing its impact\, in particular the social dynamics that m
ay result from a reading of the text. This requires endowing artificial in
telligence (AI) systems with pragmatic reasoning\, for example to correctl
y conclude that the statement “Epidemics and cases of disease in the 21st
century are “staged”” relates to unfounded conspiracy theories. In this ta
lk\, I discuss how shortcomings in the ability of current AI systems to re
ason about pragmatics present challenges to equitable detection of false o
r harmful language. I demonstrate how these shortcomings can be addressed
by imposing human-interpretable structure on deep learning architectures u
sing insights from linguistics.
In the first part of the talk\, I describe how adversarial text gen
eration algorithms can be used to improve robustness of content moderation
systems. I then introduce a pragmatic formalism for reasoning about harmf
ul implications conveyed by social media text. I show how this pragmatic a
pproach can be combined with generative neural language models to uncover
implications of news headlines. I also address the bottleneck to progress
in text generation posed by gaps in evaluation of factuality. I conclude b
y showing how context-aware content moderation can be used to ensure safe
interactions with conversational agents.
\n
Biography
\nSaadia Gabr iel is a PhD candidate in the Paul G. Allen School of Computer Scie nce & Engineering at the University of Washington\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research re volves around natural language processing and machine learning\, with a pa rticular focus on building systems for understanding how social commonsens e manifests in text (i.e. how do people typically behave in social scenari os)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outle ts like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best paper nomination and won a bes t paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Holyoke College in Computer Sc ience and Mathematics.
\n\n X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT BEGIN:VEVENT UID:ai1ec-23312@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAdvanced neural language models have grown ever large r and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretability. The black-box nature of deep neural networks blocks humans from understanding them\, as well as trusting and using them in real-world applications. This talk will introd uce interpretation techniques that bridge the gap between humans and model s for developing trustworthy natural language processing(NLP). I will firs t show how to explain black-box models and evaluate their explanations for understanding their prediction behavior. Then I will introduce how to imp rove the interpretability of neural language models by making their decisi on-making transparent and rationalized. Finally\, I will discuss how to di agnose and improve models (e.g.\, robustness) through the lens of explanat ions. I will conclude with future research directions that are centered ar ound model interpretability and committed to facilitating communications a nd interactions between intelligent machines\, system developers\, and end users for long-term trustworthy AI.\nBiography\nHanjie Chen is a Ph.D. ca ndidate in Computer Science at the University of Virginia\, advised by Pro f. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Natural Lan guage Processing (NLP)\, andInterpretable Machine Learning. She develops i nterpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Ca rlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAP WIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g. \, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Wom en & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (a s the primary instructor) co-designed and taught the course\, Interpretabl e Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teach ing Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found athttps://www.cs.virginia .edu/~hc9mx DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nAdvanced neural language models have grown ever large
r and more complex\, pushing forward the limits of language understanding
and generation\, while diminishing interpretability. The black-box nature
of deep neural networks blocks humans from understanding them\, as well as
trusting and using them in real-world applications. This talk will introd
uce interpretation techniques that bridge the gap between humans and model
s for developing trustworthy natural language processing
(NLP). I will first show how to explain black-box models and evalua
te their explanations for understanding their prediction behavior. Then I
will introduce how to improve the interpretability of neural language mode
ls by making their decision-making transparent and rationalized. Finally\,
I will discuss how to diagnose and improve models (e.g.\, robustness) thr
ough the lens of explanations. I will conclude with future research direct
ions that are centered around model interpretability and committed to faci
litating communications and interactions between intelligent machines\, sy
stem developers\, and end users for long-term trustworthy AI.
Hanjie Chen is a Ph.D. candidate in Compute r Science at the University of Virginia\, advised by Prof. Yangfeng Ji. He r research interests lie in Trustworthy AI\, Natural Language Processing ( NLP)\, and
\n X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-23910@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEffective communication lies at the heart of social h armony and individual well-being. However\, key areas of our society face profound challenges in how we talk about things\, or to each other. In thi s talk\, I will show how these challenges manifest: from the manner in whi ch TV reporters discuss current events to online health discussions in ban ned Reddit communities\, and interactions between law enforcement and comm unities of color during routine car stops. My research applies theories fr om linguistics and psychology to analyze patterns in such dialogue using l arge language models (LLMs)\, statistics\, and experimental design. In thi s presentation\, I will introduce three research studies that highlight ho w specific patterns in our language choices are predictive of real-world o utcomes. First\, I will illustrate how partisan divides in the language of America’s two major broadcasting news stations over the past decade direc tly correlate with semantic polarity trends on Twitter\, empirically linki ng for the first time how online discussions are influenced by televised m edia. Second\, I will show how “gists” or causal statements in social medi a discussions about pandemic health practices unveil underlying beliefs an d attitudes\, which in turn\, can forecast broader health trends across th e U.S. Finally\, by examining the linguistic interactions captured from th ousands of footages from police body-worn cameras\, I demonstrate how the first 45 words spoken by a police officer during a car stop with a Black d river can be quite telling about how the stop will conclude. Persistent ch allenges in dialogue marked by tensions and biases can have wide-ranging i mplications for both individuals and society. These studies call for a bro ader awareness on the influence of our language choices across institution al\, media\, and online contexts.\n\nBio\n\n\nEugenia Rho is an Assistant Professor of Computer Science at Virginia Tech\, where she leads the SAIL (Society + AI & Language) Lab. Her research lies at the intersection of Natural Language Processing (NLP) and Human-Computer Interaction (HCI). He r work aims to advance Computational Social Science (CSS) by using computa tional linguistics to better understand how AI-mediated systems impact int eractions across people and machines. DTSTART;TZID=America/New_York:20231103T120000 DTEND;TZID=America/New_York:20231103T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Eugenia Rho (Virginia Tech) “Words Matter: How Language Choices Pre dict Societal Trends and Outcomes in Media\, Health and Policing” URL:https://www.clsp.jhu.edu/events/eugenia-rho-virginia-tech/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n Interpretable Machine Learning. She dev elops interpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Carlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conference s (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Women & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (as the primary instructor) co-designed and taught the course\, Inter pretable Machine Learning\, and was awarded the UVA CS Outstanding Graduat e Teaching Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mxAbstr act
\nAbstr act
\nMultilingual machine translation has proven immensely useful for both parameter efficiency and overall perf ormance for many language pairs via complete parameter sharing. However\, some language pairs in multilingual models can see worse performance than in bilingual models\, especially in the one-to-many translation setting. M otivated by their empirical differences\, we examine the geometric differe nces in representations from bilingual models versus those from one-to-man y multilingual models. Specifically\, we measure the isotropy of these rep resentations using intrinsic dimensionality and IsoScore\, in order to mea sure how these representations utilize the dimensions in their underlying vector space. We find that for a given language pair\, its multilingual mo del decoder representations are consistently less isotropic than comparabl e bilingual model decoder representations. Additionally\, we show that muc h of this anisotropy in multilingual decoder representations can be attrib uted to modeling language-specific information\, therefore limiting remain ing representational capacity.
\n X-TAGS;LANGUAGE=en-US:2023\,November\,Verma END:VEVENT BEGIN:VEVENT UID:ai1ec-24157@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn this talk\, I will present a simple extension of i mage-based Masked Autoencoders (MAE) to self-supervised representation lea rning from audio spectrograms. Following the Transformer encoder-decoder d esign in MAE\, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio\, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens\, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder\, as au dio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target dataset s. Empirically\, Audio-MAE sets new state-of-the-art performance on six au dio and speech classification tasks\, outperforming other recent models th at use external supervised pre-training.\nBio\nFlorian Metze is a Research Scientist Manager at Meta AI in New York\, supporting a team of researche rs and engineers working on multi-modal (image\, video\, audio\, text) con tent understanding for Meta’s Family of Apps (Instagram\, Threads\, Facebo ok\, WhatsApp). He used to be an Associate Research Professor at Carnegie Mellon University\, in the School of Computer Science’s Language Technolog ies Institute\, where he still is an Adjunct Professor. He is also a co-fo under of Abridge\, a company working on extracting information from doctor patient conversations. His work covers many areas of speech recognition a nd multi-media analysis with a focus on end-to-end deep learning. Currentl y\, he focuses on multi-modal processing of videos\, and using that inform ation to recommend unconnected content. In the past\, he has worked on low resource and multi-lingual speech processing\, speech recognition with ar ticulatory features\, large-scale multi-media retrieval and summarization\ , information extraction from medical interviews\, and recognition of pers onality or similar meta-data from speech.\nFor more information\, please s ee http://www.cs.cmu.edu/directory/fmetze\n DTSTART;TZID=America/New_York:20231110T120000 DTEND;TZID=America/New_York:20231110T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Florian Metze (CMU) “Masked Autoencoders that Listen” URL:https://www.clsp.jhu.edu/events/florian-metze-cmu/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nIn this talk\, I will present a simple extension of i mage-based Masked Autoencoders (MAE) to self-supervised representation lea rning from audio spectrograms. Following the Transformer encoder-decoder d esign in MAE\, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio\, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens\, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder\, as au dio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target dataset s. Empirically\, Audio-MAE sets new state-of-the-art performance on six au dio and speech classification tasks\, outperforming other recent models th at use external supervised pre-training.
\nBio
\nFlorian Metze is a Research Scientist Manager at Meta AI in New York\ , supporting a team of researchers and engineers working on multi-modal (i mage\, video\, audio\, text) content understanding for Meta’s Family of Ap ps (Instagram\, Threads\, Facebook\, WhatsApp). He used to be an Associate Research Professor at Carnegie Mellon University\, in the School of Compu ter Science’s Language Technologies Institute\, where he still is an Adjun ct Professor. He is also a co-founder of Abridge\, a company working on ex tracting information from doctor patient conversations. His work covers ma ny areas of speech recognition and multi-media analysis with a focus on en d-to-end deep learning. Currently\, he focuses on multi-modal processing o f videos\, and using that information to recommend unconnected content. In the past\, he has worked on low resource and multi-lingual speech process ing\, speech recognition with articulatory features\, large-scale multi-me dia retrieval and summarization\, information extraction from medical inte rviews\, and recognition of personality or similar meta-data from speech.< /p>\n
For more information\, please see http://www.cs.cmu.edu/directory/fmetze
\n\n X-TAGS;LANGUAGE=en-US:2023\,Metze\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24159@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231113T120000 DTEND;TZID=America/New_York:20231113T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Kate Sanders URL:https://www.clsp.jhu.edu/events/student-seminar-kate-sanders/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,November\,Sanders END:VEVENT BEGIN:VEVENT UID:ai1ec-24163@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe almost unlimited multimedia content available on video-sharing websites has opened new challenges and opportunities for bui lding robust multimodal solutions. This seminar will describe our novel mu ltimodal architectures that (1) are robust to missing modalities\, (2) can identify noisy or less discriminative features\, and (3) can leverage unl abeled data. First\, we present a strategy that effectively combines auxil iary networks\, a transformer architecture\, and an optimized training mec hanism for handling missing features. This problem is relevant since it is expected that during inference the multimodal system will face cases with missing features due to noise or occlusion. We implement this approach fo r audiovisual emotion recognition achieving state-of-the-art performance. Second\, we present a multimodal framework for dealing with scenarios char acterized by noisy or less discriminative features. This situation is comm only observed in audiovisual automatic speech recognition (AV-ASR) with cl ean speech\, where the performance often drops compared to a speech-only s olution due to the variability of visual features. The proposed approach i s a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features\, keeping only useful informatio n. The approach improves\, or at least\, maintains performance when visual features are used. Third\, we discuss alternative strategies to leverage unlabeled multimodal data. A promising approach is to use multimodal prete xt tasks that are carefully designed to learn better representations for p redicting a given task\, leveraging the relationship between acoustic and facial features. Another approach is using multimodal ladder networks wher e intermediate representations are predicted across modalities using later al connections. These models offer principled solutions to increase the ge neralization and robustness of common speech-processing tasks when using m ultimodal architectures. \nBio\nCarlos Busso is a Professor at the Univers ity of Texas at Dallas’s Electrical and Computer Engineering Department\, where he is also the director of the Multimodal Signal Processing (MSP) La boratory. His research interest is in human-centered multimodal machine in telligence and application\, with a focus on the broad areas of affective computing\, multimodal human-machine interfaces\, in-vehicle active safety systems\, and machine learning methods for multimodal processing. He has worked on audio-visual emotion recognition\, analysis of emotional modulat ion in gestures and speech\, designing realistic human-like virtual charac ters\, and detection of driver distractions. He is a recipient of an NSF C AREER Award. In 2014\, he received the ICMI Ten-Year Technical Impact Awar d. In 2015\, his student received the third prize IEEE ITSS Best Dissertat ion Award (N. Li). He also received the Hewlett Packard Best Paper Award a t the IEEE ICME 2011 (with J. Jain)\, and the Best Paper Award at the AAAC ACII 2017 (with Yannakakis and Cowie). He received the Best of IEEE Trans actions on Affective Computing Paper Collection in 2021 (with R. Lotfian) and the Best Paper Award from IEEE Transactions on Affective Computing in 2022 (with Yannakakis and Cowie). He received the ACM ICMI Community Servi ce Award in 2023. In 2023\, he received the Distinguished Alumni Award in the Mid-Career/Academia category by the Signal and Image Processing Instit ute (SIPI) at the University of Southern California. He is currently servi ng as an associate editor of the IEEE Transactions on Affective Computing. He is an IEEE Fellow. He is a member of the ISCA\, and AAAC and a senior member of ACM. DTSTART;TZID=America/New_York:20231117T120000 DTEND;TZID=America/New_York:20231117T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Carlos Busso (University of Texas at Dallas) “Multimodal Machine Le arning for Human-Centric Tasks” URL:https://www.clsp.jhu.edu/events/carl-busso-university-of-texas-at-dalla s-multimodal-machine-learning-for-human-centric-tasks/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nThe almost unlimited multimedia content available on video-sharing websites has opened new challenges and opportun ities for building robust multimodal solutions. This seminar will describe our novel multimodal architectures that (1) are robust to missing modalit ies\, (2) can identify noisy or less discriminative features\, and (3) can leverage unlabeled data. First\, we present a strategy that effectively c ombines auxiliary networks\, a transformer architecture\, and an optimized training mechanism for handling missing features. This problem is relevan t since it is expected that during inference the multimodal system will fa ce cases with missing features due to noise or occlusion. We implement thi s approach for audiovisual emotion recognition achieving state-of-the-art performance. Second\, we present a multimodal framework for dealing with s cenarios characterized by noisy or less discriminative features. This situ ation is commonly observed in audiovisual automatic speech recognition (AV -ASR) with clean speech\, where the performance often drops compared to a speech-only solution due to the variability of visual features. The propos ed approach is a deep learning solution with a gating layer that diminishe s the effect of noisy or uninformative visual features\, keeping only usef ul information. The approach improves\, or at least\, maintains performanc e when visual features are used. Third\, we discuss alternative strategies to leverage unlabeled multimodal data. A promising approach is to use mul timodal pretext tasks that are carefully designed to learn better represen tations for predicting a given task\, leveraging the relationship between acoustic and facial features. Another approach is using multimodal ladder networks where intermediate representations are predicted across modalitie s using lateral connections. These models offer principled solutions to in crease the generalization and robustness of common speech-processing tasks when using multimodal architectures.
\nBio
\nCarlos Busso is a Professor at the University of Tex as at Dallas’s Electrical and Computer Engineering Department\, where he i s also the director of the Multimodal Signal Processing (MSP) Laboratory. His research interest is in human-centered multimodal machine intelligence and application\, with a focus on the broad areas of affective computing\ , multimodal human-machine interfaces\, in-vehicle active safety systems\, and machine learning methods for multimodal processing. He has worked on audio-visual emotion recognition\, analysis of emotional modulation in ges tures and speech\, designing realistic human-like virtual characters\, and detection of driver distractions. He is a recipient of an NSF CAREER Awar d. In 2014\, he received the ICMI Ten-Year Technical Impact Award. In 2015 \, his student received the third prize IEEE ITSS Best Dissertation Award (N. Li). He also received the Hewlett Packard Best Paper Award at the IEEE ICME 2011 (with J. Jain)\, and the Best Paper Award at the AAAC ACII 2017 (with Yannakakis and Cowie). He received the Best of IEEE Transactions on Affective Computing Paper Collection in 2021 (with R. Lotfian) and the Be st Paper Award from IEEE Transactions on Affective Computing in 2022 (with Yannakakis and Cowie). He received the ACM ICMI Community Service Award i n 2023. In 2023\, he received the Distinguished Alumni Award in the Mid-Ca reer/Academia category by the Signal and Image Processing Institute (SIPI) at the University of Southern California. He is currently serving as an a ssociate editor of the IEEE Transactions on Affective Computing. He is an IEEE Fellow. He is a member of the ISCA\, and AAAC and a senior member of ACM.
\n X-TAGS;LANGUAGE=en-US:2023\,Busso\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24165@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231127T120000 DTEND;TZID=America/New_York:20231127T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Aleem Khan URL:https://www.clsp.jhu.edu/events/student-seminar-aleem-khan/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Khan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24241@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nOur research focuses on improving speech processing a lgorithms\, such as automatic speech recognition (ASR)\, speaker identific ation\, and depression detection\, under challenging conditions such as li mited data (for example\, children’s or clinical speech)\, mismatched cond itions (for example\, training on read speech while recognizing conversati onal speech)\, and noisy speech\, using a hybrid data-driven and knowledge -based approach. This approach requires understanding of both machine lear ning approaches and of the human speech production and perception systems. I will summarize in this talk our work on children’s ASR using self-super vised models\, detecting depression from speech signals using novel speake r disentaglement techniques\, and automating scoring of children’s reading tasks with both ASR and innovative NLP algorithms.\nBiography\nAbeer Alwa n received her Ph.D. in Electrical Engineering and Computer Science from M IT in 1992. Since then\, she has been with the ECE department at UCLA wher e she is now a Full Professor and directs the Speech Processing and Audito ry Perception Laboratory. She is the recipient of the NSF Research Initiat ion and Career Awards\, NIH FIRST Award\, UCLA-TRW Excellence in Teaching Award\, Okawa Foundation Award in Telecommunication\, and the Engineer’s C ouncil Educator Award. She is a Fellow of the Acoustical Society of Americ a\, IEEE\, and International Speech Communication Assoc. (ISCA). She was a Fellow at the Radcliffe Institute\, Harvard University\, co-Editor in Chi ef of Speech Communication\, Associate Editor of JASA and IEEE TSALP\, a D istinguished Lecturer of ISCA\, a member of the IEEE Signal Processing Boa rd of Governers and she is currently on the advisory board of ISCA and the UCLA-Amazon Science Hub for Humanity and AI. DTSTART;TZID=America/New_York:20240202T120000 DTEND;TZID=America/New_York:20240202T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Abeer Alwan (UCLA) “Dealing with Limited Speech Data and Variabilit y: Three case studies” URL:https://www.clsp.jhu.edu/events/abeer-alwan-ucla/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nOur research focuses on improving speech processing a lgorithms\, such as automatic speech recognition (ASR)\, speaker identific ation\, and depression detection\, under challenging conditions such as li mited data (for example\, children’s or clinical speech)\, mismatched cond itions (for example\, training on read speech while recognizing conversati onal speech)\, and noisy speech\, using a hybrid data-driven and knowledge -based approach. This approach requires understanding of both machine lear ning approaches and of the human speech production and perception systems. I will summarize in this talk our work on children’s ASR using self-super vised models\, detecting depression from speech signals using novel speake r disentaglement techniques\, and automating scoring of children’s reading tasks with both ASR and innovative NLP algorithms.
\nBiogra phy
\nAbeer Alwan received her Ph.D. in Electrical Engineer ing and Computer Science from MIT in 1992. Since then\, she has been with the ECE department at UCLA where she is now a Full Professor and directs t he Speech Processing and Auditory Perception Laboratory. She is the recipi ent of the NSF Research Initiation and Career Awards\, NIH FIRST Award\, U CLA-TRW Excellence in Teaching Award\, Okawa Foundation Award in Telecommu nication\, and the Engineer’s Council Educator Award. She is a Fellow of t he Acoustical Society of America\, IEEE\, and International Speech Communi cation Assoc. (ISCA). She was a Fellow at the Radcliffe Institute\, Harvar d University\, co-Editor in Chief of Speech Communication\, Associate Edit or of JASA and IEEE TSALP\, a Distinguished Lecturer of ISCA\, a member of the IEEE Signal Processing Board of Governers and she is currently on the advisory board of ISCA and the UCLA-Amazon Science Hub for Humanity and A I.
\n X-TAGS;LANGUAGE=en-US:2024\,Alwan\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-24425@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\nOver the past three decades\, the fields of automat ic speech recognition (ASR) and machine translation (MT) have witnessed re markable advancements\, leading to exciting research directions such as sp eech-to-text translation (ST). This talk will delve into the domain of con versational ST\, an essential facet of daily communication\, which present s unique challenges including spontaneous informal language\, the presence of disfluencies\, high context dependence and a scarcity of ST paired dat a.\n\nConversational speech is notably characterized by its reliance on sh ort segments\, requiring the integration of broader contexts to maintain c onsistency and improve the translation’s fluency and quality. Incorporati ng longer contexts has been shown to benefit machine translation\, but the inclusion of context in E2E-ST remains under-studied. Previous approaches have used simple concatenation of audio inputs for context\, leading to m emory bottlenecks\, especially in self-attention networks\, due to the enc oding of lengthy audio segments.\n\nFirst\, I will describe how to integra te the context into E2E-ST with minimum additional memory cost. Then\, I will discuss the challenges of incorporating context in an E2E-ST system w ith limited data during training and inference and propose solutions to ov ercome them. Afterward\, I will illustrate the impact of context size and the inclusion of speaker information on performance. Lastly\, I will demon strate the benefits of context in conversational settings focusing on asp ects like anaphora resolution and the identification of named entities. DTSTART;TZID=America/New_York:20240205T120000 DTEND;TZID=America/New_York:20240205T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Amir Hussein “Towards End-to-End Conversational Speech Translation” URL:https://www.clsp.jhu.edu/events/amir-hussein-towards-end-to-end-convers ational-speech-translation/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr
act
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Over the past three decades\, the fields of automatic speech recognition (ASR) and machine tra nslation (MT) have witnessed remarkable advancements\, leading to exciting research directions such as speech-to-text translation (ST). This talk wi ll delve into the domain of conversational ST\, an essential facet of dail y communication\, which presents unique challenges including spontaneous i nformal language\, the presence of disfluencies\, high context dependence and a scarcity of ST paired data.
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\nThere is an enormous data gap between how AI systems and children learn language: The best LLMs now learn language from text with a word count in the trillions\, whereas it would take a child roughly 100K years to reach those numbers through speec h (Frank\, 2023\, “Bridging the data gap”). There is also a clear generali zation gap: whereas machines struggle with systematic generalization\, peo ple excel. For instance\, once a child learns how to “skip\,” they immedia tely know how to “skip twice” or “skip around the room with their hands up ” due to their compositional skills. In this talk\, I’ll describe two case studies in addressing these gaps:
\n1) The dat a gap: We train deep neural networks from scratch (using DINO\, CLIP\, etc .)\, not on large-scale data from the web\, but through the eyes and ears of a single child. Using head-mounted video recordings from a child (61 ho urs of video slices over 19 months)\, we show how deep neural networks can acquire many word-referent mappings\, generalize to novel visual referent s\, and achieve multi-modal alignment. Our results demonstrate how today’s AI models are capable of learning key aspects of children’s early knowled ge from realistic input.
\n2) The generalizatio n gap: Can neural networks capture human-like systematic generalization? W e address a 35-year-old debate catalyzed by Fodor and Pylyshyn’s classic a rticle\, which argued that standard neural networks are not viable models of the mind because they lack systematic compositionality — the algebraic ability to understand and produce novel combinations from known components . We’ll show how neural network can achieve human-like systematic generali zation when trained through meta-learning for compositionality (MLC)\, a n ew method for optimizing the compositional skills of neural networks throu gh practice. With MLC\, a neural network can match human performance and s olve several machine learning benchmarks.
\nGiv en this work\, we’ll discuss the paths forward for building machines that learn\, generalize\, and interact in more human-like ways based on more na tural input.
\nRelated articles:
\nVong\, W. K.\, Wang\, W.\, Orhan\, A. E.\, and Lake\, B. M (2024). Grounded language acquisition through the eyes and ears of a singl e child. Science\, 383.
\nOrhan\, A. E.\ , and Lake\, B. M. (in press). Learning high-level visual representations from a child’s perspective without strong inductive biases. Nature Mach ine Intelligence.
\nLake\, B. M. and Baroni \, M. (2023). Human-like systematic generalization through a meta-learning neural network. Nature\, 623\, 115-121.
\nBiography< /strong>
\nBrenden M. Lake is an Assistant Prof essor of Psychology and Data Science at New York University. He received h is M.S. and B.S. in Symbolic Systems from Stanford University in 2009\, an d his Ph.D. in Cognitive Science from MIT in 2014. He was a postdoctoral D ata Science Fellow at NYU from 2014-2017. Brenden is a recipient of the Ro bert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive S cience\, he is a MIT Technology Review Innovator Under 35\, and his resear ch was selected by Scientific American as one of the 10 most important adv ances of 2016. Brenden’s research focuses on computational problems that a re easier for people than they are for machines\, such as learning new con cepts\, creating new concepts\, learning-to-learn\, and asking questions.< /p>\n
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\nLarge language models like ChatGPT have shown extraor dinary abilities for writing. While impressive at first glance\, large lan guage models aren’t perfect and often make mistakes humans would not make. The main architecture behind ChatGPT mostly doesn’t differ from early neu ral networks\, and as a consequence\, carries some of the same limitations . My work revolves around the use of neural networks like ChatGPT mixed wi th symbolic methods from early AI and how these two families of methods ca n combine to create more robust AI. I talk about some of the neurosymbolic methods I used for applications in story generation and understanding — w ith the goal of eventually creating AI that can play Dungeons & Dragons. I also discuss pain points that I found for improving accessible communicat ion and show how large language models can supplement such communication.< /p>\n
Biography
\nAbstr act
\nWe introduce STAR (Stream Transduction with Anchor Re presentations)\, a novel Transformer-based model designed for efficient se quence-to-sequence transduction over streams. STAR dynamically segments in put streams to create compressed anchor representations\, achieving nearly lossless compression (12x) in Automatic Speech Recognition (ASR) and outp erforming existing methods. Moreover\, STAR demonstrates superior segmenta tion and latency-quality trade-offs in simultaneous speech-to-text tasks\, optimizing latency\, memory footprint\, and quality.
\n X-TAGS;LANGUAGE=en-US:2024\,February\,Tan END:VEVENT BEGIN:VEVENT UID:ai1ec-24429@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nI discuss the application of Foundation Models in Ast ronomy through the collaborative efforts of the UniverseTBD consortium wit h a mission to democratize Science for everyone. One of our key objectives is to overcome the limitations of general-purpose Foundation Models\, suc h as producing limited information in specialized fields. To this end\, we have developed the first specialized large language model for Astronomy\, AstroLLaMa-1. This model\, enhanced by exposure to domain-specific litera ture from the NASA Astrophysics Data System and ArXiv\, demonstrates impro ved text completion and embedding capabilities over existent GPT models. I further discuss the potential of LLMs in generating complex scientific hy potheses and extracting meaningful insights from astronomy literature. Our findings\, validated by human experts\, demonstrate the LLM capability in informed scientific critique and uncover intriguing patterns in the embed ding space\, highlighting the potential of LLMs to augment scientific inqu iry. I will also discuss preliminary work with the multi-modal model Astro LLaVA\, which allows us to interact with astronomical images via natural l anguage. Through the work of UniverseTBD\, we aim to explore how artificia l intelligence can assist human intelligence in Astronomy and\, more broad ly\, Science.\nBiography\nIoana Ciucă\, who goes by Jo\, is an interdiscip linary Jubilee Joint Fellow at the Australian National University\, workin g across the School of Computing and the Research School of Astronomy & As trophysics. Before joining ANU\, Jo finished her PhD in Astrophysics at Un iversity College London in the United Kingdom\, where she worked at the in tersection of Astronomy and Machine Learning to understand the formation a nd evolution history of our Galaxy\, the Milky Way. Jo is now focusing on utilizing foundation models that benefit researchers everywhere\, working alongside the UniverseTBD team of more than 30 astronomers\, engineers\, M L practitioners and enthusiasts worldwide.\n DTSTART;TZID=America/New_York:20240223T120000 DTEND;TZID=America/New_York:20240223T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ioana Ciuca (Australian National University)”A Universe To Be Decid ed: Towards Specialized Foundation Models for Advancing Astronomy” URL:https://www.clsp.jhu.edu/events/ioana-ciuca-australian-national-univers ity/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nI discuss the application of Foundation Models in Ast ronomy through the collaborative efforts of the UniverseTBD consortium wit h a mission to democratize Science for everyone. One of our key objectives is to overcome the limitations of general-purpose Foundation Models\, suc h as producing limited information in specialized fields. To this end\, we have developed the first specialized large language model for Astronomy\, AstroLLaMa-1. This model\, enhanced by exposure to domain-specific litera ture from the NASA Astrophysics Data System and ArXiv\, demonstrates impro ved text completion and embedding capabilities over existent GPT models. I further discuss the potential of LLMs in generating complex scientific hy potheses and extracting meaningful insights from astronomy literature. Our findings\, validated by human experts\, demonstrate the LLM capability in informed scientific critique and uncover intriguing patterns in the embed ding space\, highlighting the potential of LLMs to augment scientific inqu iry. I will also discuss preliminary work with the multi-modal model Astro LLaVA\, which allows us to interact with astronomical images via natural l anguage. Through the work of UniverseTBD\, we aim to explore how artificia l intelligence can assist human intelligence in Astronomy and\, more broad ly\, Science.
\nBiography
\nIoana Ciucă\, who goes by Jo\, is an interdisciplinary Jubilee Joint Fellow at the Australi an National University\, working across the School of Computing and the Re search School of Astronomy & Astrophysics. Before joining ANU\, Jo finishe d her PhD in Astrophysics at University College London in the United Kingd om\, where she worked at the intersection of Astronomy and Machine Learnin g to understand the formation and evolution history of our Galaxy\, the Mi lky Way. Jo is now focusing on utilizing foundation models that benefit re searchers everywhere\, working alongside the UniverseTBD team of more than 30 astronomers\, engineers\, ML practitioners and enthusiasts worldwide.< /p>\n
\n X-TAGS;LANGUAGE=en-US:2024\,Ciuca\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing stigmatizing language in medical records. DTSTART;TZID=America/New_York:20240226T120000 DTEND;TZID=America/New_York:20240226T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Keith Harrigian (JHU) “Fighting Bias From Bias: Robust Natural Lang uage Processing Techniques to Promote Health Equity” URL:https://www.clsp.jhu.edu/events/keith-harrigian-jhu-fighting-bias-from- bias-robust-natural-language-processing-techniques-to-promote-health-equit y/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing stigmatizing language in medical records.
\n X-TAGS;LANGUAGE=en-US:2024\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-24491@www.clsp.jhu.edu DTSTAMP:20240329T051754Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240401T120000 DTEND;TZID=America/New_York:20240401T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Yuan Gong URL:https://www.clsp.jhu.edu/events/yuan-gong/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Gong END:VEVENT END:VCALENDAR