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-20117@www.clsp.jhu.edu DTSTAMP:20240328T133021Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.\nBiography\nI am a n assistant professor (UD) in natural language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Langu age Learning group.\nMy work concerns the design of models and algorithms that learn to represent\, understand\, and generate language data. Example s of specific problems I am interested in include language modelling\, mac hine translation\, syntactic parsing\, textual entailment\, text classific ation\, and question answering.\nI also develop techniques to approach gen eral machine learning problems such as probabilistic inference\, gradient and density estimation.\nMy interests sit at the intersection of disciplin es such as statistics\, machine learning\, approximate inference\, global optimization\, formal languages\, and computational linguistics.\n \n DTSTART;TZID=America/New_York:20210419T120000 DTEND;TZID=America/New_York:20210419T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Wilker Aziz (University of Amsterdam) “The Inadequacy of the Mode in Neural Machine Translation” URL:https://www.clsp.jhu.edu/events/wilker-aziz-university-of-amsterdam/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.
\nBi ography
\nI am an assistant professor (UD) in natu ral language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Language Learning group.
\nMy work concerns the design of models and algorithms that learn to represe nt\, understand\, and generate language data. Examples of specific problem s I am interested in include language modelling\, machine translation\, sy ntactic parsing\, textual entailment\, text classification\, and question answering.
\nI also develop techniques to approach general machine l earning problems such as probabilistic inference\, gradient and density es timation.
\nMy interests sit at the intersection of disciplines such as statistics\, machine learning\, approximate inference\, global optimiz ation\, formal languages\, and computational linguistics.
\n\n< p> \n X-TAGS;LANGUAGE=en-US:2021\,April\,Aziz END:VEVENT BEGIN:VEVENT UID:ai1ec-20987@www.clsp.jhu.edu DTSTAMP:20240328T133021Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.\nBiography\nI’m a research scientist at the HLTCOE at Johns Hopk ins University. My primary research interests are in language generation\, diverse and constrained decoding\, and information retrieval. During my P hD I focused mainly on the task of text simplification\, and now am workin g on formulating structured prediction problems as end-to-end generation t asks. I received my PhD in July 2021 from the University of Pennsylvania w ith Chris Callison-Burch and Marianna Apidianaki. DTSTART;TZID=America/New_York:20211022T120000 DTEND;TZID=America/New_York:20211022T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Reno Kriz (HLTCOE – JHU) “Towards a Practically Useful Text Simplif ication System” URL:https://www.clsp.jhu.edu/events/reno-kriz-hltcoe-jhu-towards-a-practica lly-useful-text-simplification-system/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-21023@www.clsp.jhu.edu DTSTAMP:20240328T133021Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech data is notoriously difficult to work with due to a variety of codecs\, lengths of recordings\, and meta-data formats. W e present Lhotse\, a speech data representation library that draws upon le ssons learned from Kaldi speech recognition toolkit and brings its concept s into the modern deep learning ecosystem. Lhotse provides a common JSON d escription format with corresponding Python classes and data preparation r ecipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and cloud storage\, la zy and on-the-fly operations amongst other features. We introduce Cut and CutSet concepts\, which simplify common data wrangling tasks for audio and help incorporate acoustic context of speech utterances. Finally\, we show how Lhotse leverages PyTorch data API abstractions and adopts them to han dle speech data for deep learning.\nBiography\nPiotr Zelasko is an assista nt research scientist in the Center for Language and Speech Processing (CL SP) who specializes in automatic speech recognition (ASR) and spoken langu age understanding (SLU). His current research focuses on applying multilin gual and crosslingual speech recognition systems to categorize the phoneti c inventory of a previously unknown language and on improving defenses aga inst adversarial attacks on both speaker identification and automatic spee ch recognition systems. He is also addressing the question of how to struc ture a spontaneous conversation into high-level semantic units such as dia log acts or topics. Finally\, he is working on Lhotse + K2\, the next-gene ration speech processing research software ecosystem. Before joining Johns Hopkins\, Zelasko worked as a machine learning consultant for Avaya (2017 -2019)\, and as a machine learning engineer for Techmo (2015-2017). Zelask o received his PhD (2019) in electronics engineering\, as well as his mast er’s (2014) and undergraduate degrees (2013) in acoustic engineering from AGH University of Science and Technology in Kraków\, Poland. DTSTART;TZID=America/New_York:20211029T120000 DTEND;TZID=America/New_York:20211029T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore MD 21218 SEQUENCE:0 SUMMARY:Piotr Zelasko (CLSP at JHU) “Lhotse: a speech data representation l ibrary for the modern deep learning ecosystem” URL:https://www.clsp.jhu.edu/events/piotr-zelasko-clsp-at-jhu-lhotse-a-spee ch-data-representation-library-for-the-modern-deep-learning-ecosystem/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSpeech data is notoriously difficult t o work with due to a variety of codecs\, lengths of recordings\, and meta- data formats. We present Lhotse\, a speech data representation library tha t draws upon lessons learned from Kaldi speech recognition toolkit and bri ngs its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with corresponding Python classes and dat a preparation recipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and clo ud storage\, lazy and on-the-fly operations amongst other features. We int roduce Cut and CutSet concepts\, which simplify common data wrangling task s for audio and help incorporate acoustic context of speech utterances. Fi nally\, we show how Lhotse leverages PyTorch data API abstractions and ado pts them to handle speech data for deep learning.
\nB iography
\nPiotr Zelasko is an assistant research scientist in the Center for Language and Speech Processing (CLSP) who specializes i n automatic speech recognition (ASR) and spoken language understanding (SL U). His current research focuses on applying multilingual and crosslingual speech recognition systems to categorize the phonetic inventory of a prev iously unknown language and on improving defenses against adversarial atta cks on both speaker identification and automatic speech recognition system s. He is also addressing the question of how to structure a spontaneous co nversation into high-level semantic units such as dialog acts or topics. F inally\, he is working on Lhotse + K2\, the next-generation speech process ing research software ecosystem. Before joining Johns Hopkins\, Zelasko wo rked as a machine learning consultant for Avaya (2017-2019)\, and as a mac hine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2 019) in electronics engineering\, as well as his master’s (2014) and under graduate degrees (2013) in acoustic engineering from AGH University of Sci ence and Technology in Kraków\, Poland.
\n X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-21267@www.clsp.jhu.edu DTSTAMP:20240328T133021Z 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:20240328T133021Z 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:20240328T133021Z 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:20240328T133021Z 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-22423@www.clsp.jhu.edu DTSTAMP:20240328T133021Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20221007T120000 DTEND;TZID=America/New_York:20221007T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ariya Rastrow (Amazon) URL:https://www.clsp.jhu.edu/events/ariya-rastrow-amazon-2/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Rastrow END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T133021Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\n\nModel robustness and spurious correlations have rec eived increasing attention in the NLP community\, both in methods and eval uation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model\, as judged by domain e xperts.\n\n\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, man y features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image . In contrast\, they carry important information that’s needed to make pre dictions by humans. In this talk\, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for pred iction. We then discuss the implications of this categorization in represe ntation\, learning\, and evaluation.\nBiography\nHe He is an Assistant Pro fessor in the Department of Computer Science and the Center for Data Scien ce at New York University. She obtained her PhD in Computer Science at the University of Maryland\, College Park. Before joining NYU\, she spent a y ear at AWS AI and was a post-doc at Stanford University before that. She i s interested in building robust and trustworthy NLP systems in human-cente red settings. Her recent research focus includes robust language understan ding\, collaborative text generation\, and understanding capabilities and issues of large language models. DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
\nAbstr act
\nAbstr act
\nModern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expre ssions. In this talk\, I will give a few examples of exploring alternativ e architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and a ccessing knowledge in a dedicated memory component.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Goo gle Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collaborators in Google Research.< /p>\n
Biography
\nFei is a research scientist at Google Research. Before that\, he was a Professor of Computer Science at U niversity of Southern California. His primary research interests are machi ne learning and its application to various AI problems: speech and languag e processing\, computer vision\, robotics and recently weather forecast an d climate modeling. He has a PhD (2007) from Computer and Information Sc ience from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
\n X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23304@www.clsp.jhu.edu DTSTAMP:20240328T133021Z 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\\nAbstr act
\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
\nAbstr act
\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:20240328T133021Z 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:20240328T133021Z 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:20240328T133021Z 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:20240328T133022Z 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-23900@www.clsp.jhu.edu DTSTAMP:20240328T133022Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231002T120000 DTEND;TZID=America/New_York:20231002T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Anna Favaro URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-anna-favaro/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Favaro\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24115@www.clsp.jhu.edu DTSTAMP:20240328T133022Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nOur goal is to use AI to automatically find tax minim ization strategies\, an approach which we call “Shelter Check.” It would c ome in two variants. Existing-Authority Shelter Check would aim to find wh ether existing tax law authorities can be combined to create tax minimizat ion strategies\, so the IRS or Congress can shut them down. New-Authority Shelter Check would automate checking whether a new tax law authority – li ke proposed legislation or a draft court decision – would combine with exi sting authorities to create a new tax minimization strategy. We had initia lly had high hopes for GPT-* large language models for implementing Shelte r Check\, but our tests have showed that they do very poorly at basic lega l reasoning and handling legal text. So we are now creating a benchmark an d training data for LLM’s handling legal text\, hoping to spur improvement s. DTSTART;TZID=America/New_York:20231006T120000 DTEND;TZID=America/New_York:20231006T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Andrew Blair-Stanek “Shelter Check and GPT-4 ’s Bad Legal Performance” URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-andrew-blair-stane k-shelter-check-and-gpt-4s-bad-legal-performance/ 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
\nOur goal is to use AI to automatically find tax minim ization strategies\, an approach which we call “Shelter Check.” It would c ome in two variants. Existing-Authority Shelter Check would aim to find wh ether existing tax law authorities can be combined to create tax minimizat ion strategies\, so the IRS or Congress can shut them down. New-Authority Shelter Check would automate checking whether a new tax law authority – li ke proposed legislation or a draft court decision – would combine with exi sting authorities to create a new tax minimization strategy. We had initia lly had high hopes for GPT-* large language models for implementing Shelte r Check\, but our tests have showed that they do very poorly at basic lega l reasoning and handling legal text. So we are now creating a benchmark an d training data for LLM’s handling legal text\, hoping to spur improvement s.
\n X-TAGS;LANGUAGE=en-US:2023\,Blair-Stanek\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24005@www.clsp.jhu.edu DTSTAMP:20240328T133022Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision resear ch. These models not only generate high fidelity text or image outputs\, b ut also demonstrate impressive domain and task generalization capabilities . In contrast\, audio generative models are relatively primitive in scale and generalization.\nIn this talk\, I will start with a brief introduction on conventional neural speech generative models and discuss why they are unfit for scaling to Internet-scale data. Next\, by reviewing the latest l arge-scale generative models for text and image\, I will outline a few lin es of promising approaches to build scalable speech models. Last\, I will present Voicebox\, our latest work to advance this area. Voicebox is the m ost versatile generative model for speech. It is trained with a simple tas k — text conditioned speech infilling — on over 50K hours of multilingual speech with a powerful flow-matching objective. Through in-context learnin g\, Voicebox can perform monolingual/cross-lingual zero-shot TTS\, holisti c style conversion\, transient noise removal\, content editing\, and diver se sample generation. Moreover\, Voicebox achieves state-of-the-art perfor mance and excellent run-time efficiency.\nBiography\nWei-Ning Hsu is a res earch scientist at Meta Foundational AI Research (FAIR) and currently the lead of the audio generation team. His research focuses on self-supervised learning and generative models for speech and audio. His pioneering work includes HuBERT\, AV-HuBERT\, TextlessNLP\, data2vec\, wav2vec-U\, textles s speech translation\, and Voicebox. \nPrior to joining Meta\, Wei-Ning wo rked at MERL and Google Brain as a research intern. He received his Ph.D. and S.M. degrees in Electrical Engineering and Computer Science from Massa chusetts Institute of Technology in 2020 and 2018\, under the supervision of Dr. James Glass. He received his B.S. degree in Electrical Engineering from National Taiwan University in 2014\, under the supervision of Prof. L in-shan Lee and Prof. Hsuan-Tien Lin. DTSTART;TZID=America/New_York:20231009T120000 DTEND;TZID=America/New_York:20231009T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei-Ning Hsu (Meta Foundational AI Research) “Large Scale Universal Speech Generative Models” URL:https://www.clsp.jhu.edu/events/wei-ning-hsu-meta-foundational-ai-resea rch-large-scale-universal-speech-generative-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge-scale generative models such as GPT and DALL-E have revolutionized natural langu age processing and computer vision research. These models not only generat e high fidelity text or image outputs\, but also demonstrate impressive do main and task generalization capabilities. In contrast\, audio generative models are relatively primitive in scale and generalization.
\nIn this talk\, I will st art with a brief introduction on conventional neural speech generative mod els and discuss why they are unfit for scaling to Internet-scale data. Nex t\, by reviewing the latest large-scale generative models for text and ima ge\, I will outline a few lines of promising approaches to build scalable speech models. Last\, I will present Voicebox\, our latest work to advance this area. Voicebox is the most versatile generative model for speech. It is trained with a simple task — text conditioned speech infilling — on ov er 50K hours of multilingual speech with a powerful flow-matching objectiv e. Through in-context learning\, Voicebox can perform monolingual/cross-li ngual zero-shot TTS\, holistic style conversion\, transient noise removal\ , content editing\, and diverse sample generation. Moreover\, Voicebox ach ieves state-of-the-art performance and excellent run-time efficiency.
\nBiography
\nWei-Ning Hsu is a research scientist at Meta Founda tional AI Research (FAIR) and currently the lead of the audio generation t eam. His research focuses on self-supervised learning and generative model s for speech and audio. His pioneering work includes HuBERT\, AV-HuBERT\, TextlessNLP\, data2vec\, wav2vec-U\, textless speech translation\, and Voi cebox.
\nPri or to joining Meta\, Wei-Ning worked at MERL and Google Brain as a researc h intern. He received his Ph.D. and S.M. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2020 a nd 2018\, under the supervision of Dr. James Glass. He received his B.S. d egree in Electrical Engineering from National Taiwan University in 2014\, under the supervision of Prof. Lin-shan Lee and Prof. Hsuan-Tien Lin.
\n X-TAGS;LANGUAGE=en-US:2023\,Hsu\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23902@www.clsp.jhu.edu DTSTAMP:20240328T133022Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nPretrained language models (LMs) encode implicit repr esentations of knowledge in their parameters. Despite this observation\, o ur best methods for interpreting these representations yield few actionabl e insights on how to manipulate this parameter space for downstream benefi t. In this talk\, I will present work on methods that simulate machine rea soning by localizing and modifying parametric knowledge representations. F irst\, I will present a method for discovering knowledge-critical subnetwo rks within pretrained language models\, and show that these sparse computa tional subgraphs are responsible for the model’s ability to encode specifi c pieces of knowledge. Then\, I will present a new reasoning algorithm\, R ECKONING\, a bi-level optimisation procedure that dynamically encodes and reasons over new knowledge at test-time using the model’s existing learned knowledge representations as a scratchpad. Finally\, I will discuss next steps and challenges in using internal model mechanisms for reasoning.\n\n Bio\n\nAntoine Bosselut is an assistant professor in the School of Compute r and Communication Sciences at the École Polytechnique Fédéral de Lausann e (EPFL). He was a postdoctoral scholar at Stanford University and a Young Investigator at the Allen Institute for AI (AI2). He completed his PhD at the University of Washington and was a student researcher at Microsoft Re search. His research interests are in building systems that mix knowledge and language representations to solve problems in NLP\, specializing in co mmonsense representation and reasoning. DTSTART;TZID=America/New_York:20231013T120000 DTEND;TZID=America/New_York:20231013T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Antoine Bosselut (EPFL) “From Mechanistic Interpretability to Mecha nistic Reasoning” URL:https://www.clsp.jhu.edu/events/antoine-bosselut-epfl/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAbstr act
\nRecent advances in speech technology make heavy use o f pre-trained models that learn from large quantities of raw (untranscribe d) speech\, using “self-supervised” (ie unsupervised) learning. These mode ls learn to transform the acoustic input into a different representational format that makes supervised learning (for tasks such as transcription or even translation) much easier. However\, *what* and *how* speech-relevant information is encoded in these representations is not well understood. I will talk about some work at various stages of completion in which my gro up is analyzing the structure of these representations\, to gain a more sy stematic understanding of how word-level\, phonetic\, and speaker informat ion is encoded.
\nBiography
\nSharon Goldwate
r is a Professor in the Institute for Language\, Cognition and Computation
at the University of Edinburgh’s School of Informatics. She received her
PhD in 2007 from Brown University and spent two years as a postdoctoral re
searcher at Stanford University before moving to Edinburgh. Her research i
nterests include unsupervised and minimally-supervised learning for speech
and language processing\, computer modelling of language acquisition in c
hildren\, and computational studies of language use. Her main focus withi
n linguistics has been on the lower levels of structure including phonetic
s\, phonology\, and morphology.
Prof. Goldwater has received awards including the 2016 Roger Needha
m Award from the British Computer Society for “distinguished research cont
ribution in computer science by a UK-based researcher who has completed up
to 10 years of post-doctoral research.” She has served on the editorial b
oards of several journals\, including Computational Linguistics\, Transact
ions of the Association for Computational Linguistics\, and the inaugural
board of OPEN MIND: Advances in Cognitive Science. She was a program chair
for the EACL 2014 Conference and chaired the EACL governing board from 20
19-2020.
Abstr 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:20240328T133022Z 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
\n
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.
\n\nAbstr act
\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
\\nAbstr act
\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:20240328T133022Z 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:20240328T133022Z 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 END:VCALENDAR