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-20115@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).\nBiography\nJiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology\, wher e he leads a multidisciplinary team of researchers (signal processing engi neers\, data scientists\, neurologists\, psychologists) with a special foc us on the development of new digital endpoints and digital biomarkers enab ling to better understand\, diagnose and monitor neurodegenerative (e.g. P arkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases. DTSTART;TZID=America/New_York:20210329T120000 DTEND;TZID=America/New_York:20210329T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Jiri Mekyska (Brno University of Technology) “Data Science in Small Medical Data Sets: From Logistic Regression Towards Logistic Regression” URL:https://www.clsp.jhu.edu/events/jiri-mekyska-brno-university-of-technol ogy/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).
\nBiography
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\nOver the last few years\, deep neural models have tak en over the field of natural language processing (NLP)\, brandishing great improvements on many of its sequence-level tasks. But the end-to-end natu re of these models makes it hard to figure out whether the way they repres ent individual words aligns with how language builds itself from the botto m up\, or how lexical changes in register and domain can affect the untest ed aspects of such representations.
\nIn this talk\, I will present NYTWIT\, a dataset created to challenge large language models at the lexic al level\, tasking them with identification of processes leading to the fo rmation of novel English words\, as well as with segmentation and recovery of the specific subclass of novel blends. I will then present XRayEmb\, a method which alleviates the hardships of processing these novelties by fi tting a character-level encoder to the existing models’ subword tokenizers \; and conclude with a discussion of the drawbacks of current tokenizers’ vocabulary creation schemes.
\nBiography
\nYuval Pinter
is a Senior Lecturer in the Department of Computer Science at Ben-Gurion
University of the Negev\, focusing on natural language processing. Yuval got his PhD at the Georgia Institute of Tec
hnology School of Interactive Computing as a Bloomberg Data Science PhD Fe
llow. Before that\, he worked as a Research Engineer at Yahoo Labs and as
a Computational Linguist at Ginger Software\, and obtained an MA in Lingui
stics and a BSc in CS and Mathematics\, both from Tel Aviv University.
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\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon. p>\n
Biography
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\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.
\nBiography
\nDamianos Karakos has been at Raytheon BBN for the past nine years\, wh ere he is currently a Senior Principal Engineer\, Research. Before that\, he was research faculty at Johns Hopkins University. He has worked on seve ral Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IA RPA MATERIAL\, IARPA BETTER) and on a variety of HLT-related topics (e.g.\ , speech recognition\, speech activity detection\, keyword search\, inform ation retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of human language technology an d machine learning\, with an emphasis on statistical methods. He obtained a PhD in Electrical Engineering from the University of Maryland\, College Park\, in 2002.
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\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security thr eat to the state-of-the-art speech and speaker recognition systems\, makin g it vital to propose countermeasures against them. In this talk\, we focu s on 1) classification of a given adversarial attack into attack algorithm type\, threat model type\, and signal-to-adversarial-noise ratios\, 2) de veloping a novel speech denoising solution to further improve the classifi cation performance.
\nOur proposed approach uses a n x-vector network as a signature extractor to get embeddings\, which we c all signatures. These signatures contain information about the attack and can help classify different attack algorithms\, threat models\, and signal -to-adversarial-noise ratios. We demonstrate the transferability of such s ignatures to other tasks. In particular\, a signature extractor trained to classify attacks against speaker identification can also be used to class ify attacks against speaker verification and speech recognition. We also s how that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature e xtractor by making the job of the signature extractor easier by removing t he clean signal from the adversarial example (which consists of clean sign al+perturbation). We train our signature extractor using adversarial pertu rbation. At inference time\, we use a time-domain denoiser to obtain adver sarial perturbation from adversarial examples. Using our improved approach \, we show that common attacks in the literature (Fast Gradient Sign Metho d (FGSM)\, Projected Gradient Descent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks w ith an equal error rate (EER) of about 9%\, which is very promising.
\n X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-21615@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\n\nWe consider a problem of data collection for sema ntically rich NLU tasks\, where detailed semantics of documents (or uttera nces) are captured using a complex meaning representation. Previously\, d ata collection for such tasks was either handled at the cost of extensive annotator training (e.g. in FrameNet or PropBank) or simplified meaning re presentation (e.g. in QA-SRL or Overnight). In this talk\, we present two systems [1\, 2] that aim to support fast\, accurate\, and expressive sema ntic annotations by pairing human workers with a trained model in the loop .\n\nThe first system\, called Guided K-best [1]\, is an annotation toolki t for conversational semantic parsing. Instead of typing annotations from scratch\, data specialists choose a correct parse from the K-best output of a few-shot prototyped model. As the K-best list can be large (e.g. K=1 00)\, we guide the annotators’ exploration of the K-best list via explaina ble hierarchical clustering. In addition\, we experiment with RoBERTa-bas ed reranking of the K-best list to recalibrate the few-shot model towards Accuracy@K. The final system allows to annotate data up to 35% faster tha n the standard\, non-guided K-best and improves the few-shot model’s top-1 accuracy by up to 18%. The second system\, called SchemaBlocks [2]\, is an annotation toolkit for schemas\, or structured descriptions of frequent real-world scenarios (e.g.\, cooking a meal). It represents schemas in t he annotation UI as nested blocks. Using a novel Causal ARM model\, we fu rther speed up the annotation process and guide data specialists towards e xpressive and diverse schemas. As part of this work\, we collect 232 sche mas\, evaluating their internal coherence and their coverage on large-scal e newswire corpora.\n\n\n DTSTART;TZID=America/New_York:20220311T120000 DTEND;TZID=America/New_York:20220311T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Student Seminar – Anton Belyy “Systems for Human-AI Cooperation on Collecting Semantic Annotations” URL:https://www.clsp.jhu.edu/events/student-seminar-anton-belyy-systems-for -human-ai-cooperation-on-collecting-semantic-annotations/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\n\n X-TAGS;LANGUAGE=en-US:2022\,Belyy\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-21621@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.\nBiography\nAlane Su hr is a PhD Candidate in the Department of Computer Science at Cornell Uni versity\, advised by Yoav Artzi. Her research spans natural language proc essing\, machine learning\, and computer vision\, with a focus on building systems that participate and continually learn in situated natural langua ge interactions with human users. Alane’s work has been recognized by pape r awards at ACL and NAACL\, and has been supported by fellowships and gran ts\, including an NSF Graduate Research Fellowship\, a Facebook PhD Fellow ship\, and research awards from AI2\, ParlAI\, and AWS. Alane has also co- organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as an Eminence Fellow at the Ohio State University. DTSTART;TZID=America/New_York:20220314T120000 DTEND;TZID=America/New_York:20220314T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Alane Suhr (Cornell University) “Reasoning and Learning in Interact ive Natural Language Systems” URL:https://www.clsp.jhu.edu/events/alane-suhr-cornell-university-reasoning -and-learning-in-interactive-natural-language-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.
\nBiog raphy
\nAlane Suhr is a PhD Candidate in the Department of Computer Science at Cornell University\, advised by Yoav Artzi. Her resea rch spans natural language processing\, machine learning\, and computer vi sion\, with a focus on building systems that participate and continually l earn in situated natural language interactions with human users. Alane’s w ork has been recognized by paper awards at ACL and NAACL\, and has been su pported by fellowships and grants\, including an NSF Graduate Research Fel lowship\, a Facebook PhD Fellowship\, and research awards from AI2\, ParlA I\, and AWS. Alane has also co-organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane receive d a BS in Computer Science and Engineering as an Eminence Fellow at the Oh io State University.
\n X-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT BEGIN:VEVENT UID:ai1ec-21616@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\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 shown the absence of appropriate tu ning\, specifically in the presence of semantic shift\, can hinder robustn ess of the underlying methods. However\, little is known about the practic al effect this sensitivity may have on downstream longitudinal analyses. W e explore this gap in the literature through a timely case study: understa nding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable featur es can promote significant changes in longitudinal estimates of our target outcome. At the same time\, we demonstrate that a recently-introduced met hod for measuring semantic shift may be used to proactively identify failu re points of language-based models and\, in turn\, improve predictive gene ralization. DTSTART;TZID=America/New_York:20220318T120000 DTEND;TZID=America/New_York:20220318T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\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 shown the absence of appropriate tu ning\, specifically in the presence of semantic shift\, can hinder robustn ess of the underlying methods. However\, little is known about the practic al effect this sensitivity may have on downstream longitudinal analyses. W e explore this gap in the literature through a timely case study: understa nding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable featur es can promote significant changes in longitudinal estimates of our target outcome. At the same time\, we demonstrate that a recently-introduced met hod for measuring semantic shift may be used to proactively identify failu re points of language-based models and\, in turn\, improve predictive gene ralization.
\n X-TAGS;LANGUAGE=en-US:2022\,Harrigian\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-21497@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile the “deep learning tsunami” continues to define the state of the art in speech and language processing\, finite-state tra nsducer grammars developed by linguists and engineers are still widely use d in industrial\, highly-multilingual settings\, particularly for symbolic \, “front-end” speech applications. In this talk\, I will first briefly re view the current state of the OpenFst and OpenGrm finite-state transducer libraries. I then review two “late-breaking” algorithms found in these lib raries. The first is a heuristic but highly-effective general-purpose opti mization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-deterministic weighted accepto rs which lack certain properties required by classic shortest-path algorit hms. I will then illustrate how the OpenGrm tools can be used to induce a finite-state string-to-string transduction model known as a pair n-gram mo del. This model has been applied to grapheme-to-phoneme conversion\, loanw ord detection\, abbreviation expansion\, and back-transliteration\, among other tasks.\nBiography\nKyle Gorman is an assistant professor of linguist ics at the Graduate Center\, City University of New York\, and director of the master’s program in computational linguistics\; he is also a software engineer in the speech and language algorithms group at Google. With Rich ard Sproat\, he is the coauthor of Finite-State Text Processing (Morgan & Claypool\, 2021) and the creator of Pynini\, a finite-state text processin g library for Python. He has also published on statistical methods for com paring computational models\, text normalization\, grapheme-to-phoneme con version\, and morphological analysis\, as well as many topics in linguisti c theory. DTSTART;TZID=America/New_York:20220401T120000 DTEND;TZID=America/New_York:20220401T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Kyle Gorman (City University of New York) ” Weighted Finite-State T ransducers: The Later Years” URL:https://www.clsp.jhu.edu/events/kyle-gorman-city-university-of-new-york -weighted-finite-state-transducers-the-later-years/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nWhile the “deep learning tsunami” continues to define the state of the art in speech and language processing\, finite-state tra nsducer grammars developed by linguists and engineers are still widely use d in industrial\, highly-multilingual settings\, particularly for symbolic \, “front-end” speech applications. In this talk\, I will first briefly re view the current state of the OpenFst and OpenGrm finite-state transducer libraries. I then review two “late-breaking” algorithms found in these lib raries. The first is a heuristic but highly-effective general-purpose opti mization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-deterministic weighted accepto rs which lack certain properties required by classic shortest-path algorit hms. I will then illustrate how the OpenGrm tools can be used to induce a finite-state string-to-string transduction model known as a pair n-gram mo del. This model has been applied to grapheme-to-phoneme conversion\, loanw ord detection\, abbreviation expansion\, and back-transliteration\, among other tasks.
\nBiography
\nKyle Gorman is an assistant professor of linguistics at the Graduate Center\, City Universit y of New York\, and director of the master’s program in computational ling uistics\; he is also a software engineer in the speech and language algori thms group at Google. With Richard Sproat\, he is the coauthor of Finit e-State Text Processing (Morgan & Claypool\, 2021) and the creator of Pynini\, a finite-state text processing library for Python. He has also pu blished on statistical methods for comparing computational models\, text n ormalization\, grapheme-to-phoneme conversion\, and morphological analysis \, as well as many topics in linguistic theory.
\n X-TAGS;LANGUAGE=en-US:2022\,Gorman\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22374@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.\nBiography\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the University of Maryland\, College Par k. She holds joint appointments in the Department of Linguistics and the I nstitute for Advanced Computer Studies (UMIACS). In 2019\, Rachel complete d her Ph.D. in Computer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Inves tigator at the Allen Institute for AI in Seattle\, and a visiting research er at the University of Washington. Her research interests include computa tional semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP. DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nIn recent years\, the field of Natural Language Proce ssing has seen a profusion of tasks\, datasets\, and systems that facilita te reasoning about real-world situations through language (e.g.\, RTE\, MN LI\, COMET). Such systems might\, for example\, be trained to consider a s ituation where “somebody dropped a glass on the floor\,” and conclude it i s likely that “the glass shattered” as a result. In this talk\, I will dis cuss three pieces of work that revisit assumptions made by or about these systems. In the first work\, I develop a Defeasible Inference task\, which enables a system to recognize when a prior assumption it has made may no longer be true in light of new evidence it receives. The second work I wil l discuss revisits partial-input baselines\, which have highlighted issues of spurious correlations in natural language reasoning datasets and led t o unfavorable assumptions about models’ reasoning abilities. In particular \, I will discuss experiments that show models may still learn to reason i n the presence of spurious dataset artifacts. Finally\, I will touch on wo rk analyzing harmful assumptions made by reasoning models in the form of s ocial stereotypes\, particularly in the case of free-form generative reaso ning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Computer Science at the Unive rsity of Maryland\, College Park. She holds joint appointments in the Depa rtment of Linguistics and the Institute for Advanced Computer Studies (UMI ACS). In 2019\, Rachel completed her Ph.D. in Computer Science at Johns Ho pkins University in the Center for Language and Speech Processing. From 20 19-2020\, she was a Young Investigator at the Allen Institute for AI in Se attle\, and a visiting researcher at the University of Washington. Her res earch interests include computational semantics\, common-sense reasoning\, and issues of social bias and fairness in NLP.
\n X-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nI will present our work on data augmentation using st yle transfer as a way to improve domain adaptation in sequence labeling ta sks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transform the labelled out of domain data into something that stylistically is more closely related t o the target data. Then we can train a model on a combination of the gener ated data and the smaller amount of in domain data to improve NER predicti on performance. I will show recent empirical results on these efforts.\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.\nBiography\nThamar Solorio is a Professor of Computer Science at the University of Houston (UH). She holds graduate deg rees in Computer Science from the Instituto Nacional de Astrofísica\, Ópti ca y Electrónica\, in Puebla\, Mexico. Her research interests include info rmation extraction from social media data\, enabling technology for code-s witched data\, stylistic modeling of text\, and more recently multimodal a pproaches for online content understanding. She is the director and founde r of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution\, and recipient of the 2014 Emerging L eader ABIE Award in Honor of Denice Denton. She is currently serving a sec ond term as an elected board member of the North American Chapter of the A ssociation of Computational Linguistics and was PC co-chair for NAACL 2019 . She recently joined the team of Editors in Chief for the ACL Rolling Rev iew (ARR) system. Her research is currently funded by the NSF and by ADOBE . DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nI will present our work on data a ugmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transfo rm the labelled out of domain data into something that stylistically is mo re closely related to the target data. Then we can train a model on a comb ination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results o n these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUA L (Research in Text Understanding and Analysis of Language) lab. The commo n thread among all these research problems is the scarcity of labeled data .
\nBiography
\nThamar Solorio is a Professor of Computer Science at the Univer sity of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include information extraction from social media data\, enabling technology for code-switched data\, stylistic model ing of text\, and more recently multimodal approaches for online content u nderstanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attrib ution\, and recipient of the 2014 Emerging Leader ABIE Award in Honor of D enice Denton. She is currently serving a second term as an elected board m ember of the North American Chapter of the Association of Computational Li nguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.
\n X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-22380@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.\nBio graphy\n\nAntonios Anastasopoulos is an Assistant Professor in Computer Sc ience at George Mason University. In 2019\, Antonis received his PhD in Co mputer Science from the University of Notre Dame and then worked as a post doctoral researcher at the Language Technologies Institute at Carnegie Mel lon University. His research interests revolve around computational lingui stics and natural language processing with a focus on low-resource setting s\, endangered languages\, and cross-lingual learning.\n\n\n DTSTART;TZID=America/New_York:20220930T120000 DTEND;TZID=America/New_York:20220930T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Antonios Anastasopoulos (George Mason University) “NLP Beyond the T op-100 Languages” URL:https://www.clsp.jhu.edu/events/antonis-anastasopoulos-george-mason-uni versity/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.
\nBiography
\nAntonios Anastasopoulos is an Assistant Professor in Compu ter Science at George Mason University. In 2019\, Antonis received his PhD in Computer Science from the University of Notre Dame and then worked as a postdoctoral researcher at the Language Technologies Institute at Carneg ie Mellon University. His research interests revolve around computational linguistics and natural language processing with a focus on low-resource s ettings\, endangered languages\, and cross-lingual learning.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Anastasopoulos\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23320@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.\nBiography\nJohn H.L. Hansen\, received Ph.D. & M.S. degrees from G eorgia Institute of Technology\, and B.S.E.E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 2005\, where he currently serves a s Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair i n Telecom. Engineering\, and directs Center for Robust Speech Systems (CRS S). He is an ISCA Fellow\, IEEE Fellow\, and has served as Member and TC-C hair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SL TC)\, and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He serv ed as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23 ) as Treasurer\, has supervised 99 PhD/MS thesis candidates (EE\,CE\,BME\, TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-D allas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of sp eech/language/hearing processing & technology including coauthor of textbo ok Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Speech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Ch air/Co-Chair/Tech.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEE E SLT-2014\, ISCA INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIO US SERVICE Award.\n DTSTART;TZID=America/New_York:20230303T120000 DTEND;TZID=America/New_York:20230303T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:John Hansen (University of Texas at Dallas) “Challenges and Advance ments in Speaker Diarization & Recognition for Naturalistic Data Streams” URL:https://www.clsp.jhu.edu/events/john-hansen-university-of-texas-at-dall as/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.
\nBiography
\nJohn H.L. Hansen\, recei ved Ph.D. & M.S. degrees from Georgia Institute of Technology\, and B.S.E. E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 200 5\, where he currently serves as Associate Dean for Research\, Prof. of EC E\, Distinguished Univ. Chair in Telecom. Engineering\, and directs Center for Robust Speech Systems (CRSS). He is an ISCA Fellow\, IEEE Fellow\, an d has served as Member and TC-Chair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SLTC)\, and Technical Advisor to U.S. Delegat e for NATO (IST/TG-01). He served as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23) as Treasurer\, has supervised 99 PhD/MS t hesis candidates (EE\,CE\,BME\,TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sc i)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Researc h Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of speech/language/hearing processing & technolo gy including coauthor of textbook Discrete-Time Processing of Speech Signa ls\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Sp eech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 200 0). He served as Organizer\, Chair/Co-Chair/Tech.Chair for ISCA INTERSPEEC H-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISCA INTERSPEECH-2002\, and Te ch. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processin g Society Leo Beranek MERITORIOUS SERVICE Award.
\n\n X-TAGS;LANGUAGE=en-US:2023\,Hansen\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23439@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs data-based technologies proliferate\, it is increa singly important for researchers to be aware of their work’s wider impact. Concerns like navigating the IRB and figuring out copyright and licensing issues are still key\, but the current focus shift to matters like inclus ivity\, fairness\, and transparency and their impact on the research/devel opment life cycle have added complexity to the research task. In this talk \, we will take a broad look at the various ways ethics intersects with na tural language processing\, machine learning\, and artificial intelligence research and discuss strategies and resources for managing these concerns within the broader research framework.\nBiography\nDenise is responsible for the overall operation of LDC’s External Relations group which includes intellectual property management\, licensing\, regulatory matters\, publi cations\, membership and communications. Before joining LDC\, she practice d law for over 20 years in the areas of international trade\, intellectual property and commercial litigation. She has an A.B. in Political Science from Bryn Mawr College and a Juris Doctor degree from the University of Mi ami School of Law. DTSTART;TZID=America/New_York:20230310T120000 DTEND;TZID=America/New_York:20230310T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street SEQUENCE:0 SUMMARY:Denise DiPersio (Linguistic Data Consortium\, University of Pennsyl vania) “Data and Ethics: Where Does the Twain Meet?” URL:https://www.clsp.jhu.edu/events/denise-dipersio-linguistic-data-consort ium-university-of-pennsylvania-data-and-ethics-where-does-the-twain-meet/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nAs data-based technologies proliferate\, it is increa singly important for researchers to be aware of their work’s wider impact. Concerns like navigating the IRB and figuring out copyright and licensing issues are still key\, but the current focus shift to matters like inclus ivity\, fairness\, and transparency and their impact on the research/devel opment life cycle have added complexity to the research task. In this talk \, we will take a broad look at the various ways ethics intersects with na tural language processing\, machine learning\, and artificial intelligence research and discuss strategies and resources for managing these concerns within the broader research framework.
\nBiography
\nDenise is responsible for the overall operation of LDC’s External Relations group which includes intellectual property management\, licensi ng\, regulatory matters\, publications\, membership and communications. Be fore joining LDC\, she practiced law for over 20 years in the areas of int ernational trade\, intellectual property and commercial litigation. She ha s an A.B. in Political Science from Bryn Mawr College and a Juris Doctor d egree from the University of Miami School of Law.
\n X-TAGS;LANGUAGE=en-US:2023\,DiPersio\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23505@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecent advances in large pretrained language models h ave unlocked new exciting applications for Natural Language Generation for creative tasks\, such as lyrics or humour generation. In this talk we wil l discuss recent works by our team at Alexa AI and discuss current challen ges: (1) Pun understanding and generation: We release new datasets for pun understanding and the novel task of context-situated pun generation\, and demonstrate the value of our annotations for pun classification and gener ation tasks. (2) Song lyric generation: we design a hierarchical lyric gen eration framework that enables us to generate pleasantly-singable lyrics w ithout training on melody-lyric aligned data\, and show that our approach is competitive with strong baselines supervised on parallel data. (3) Crea te with Alexa: a multimodal story creation experience recently launched on Alexa devices\, which leverages story text generation models in tandem wi th story visualization and background music generation models to produce m ultimodal stories for kids.\nBiography\nAlessandra Cervone is an Applied S cientist in the Natural Understanding team at Amazon Alexa AI. Alessandra holds an MSc in Speech and Language Processing from University of Edinburg h and a PhD in CS from University of Trento (Italy). During her PhD\, Ales sandra worked on computational models of coherence in open-domain dialogue advised by Giuseppe Riccardi. In the first year of the PhD\, she was the team leader of one of the teams selected to compete in the first edition o f the Alexa Prize. More recently\, her research interests have been focuse d on natural language generation and its evaluation\, in particular in the context of creative AI applications. DTSTART;TZID=America/New_York:20230317T120000 DTEND;TZID=America/New_York:20230317T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Alessandra Cervone (Amazon) “Controllable Text Generation for Creat ive Applications URL:https://www.clsp.jhu.edu/events/alexxandra-cervone-amazon/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nRecent advances in large pretrain ed language models have unlocked new exciting applications for Natural Lan guage Generation for creative tasks\, such as lyrics or humour generation. In this talk we will discuss recent works by our team at Alexa AI and dis cuss current challenges: (1) Pun understanding and generation: We release new datasets for pun understanding and the novel task of context-situated pun generation\, and demonstrate the value of our annotations for pun clas sification and generation tasks. (2) Song lyric generation: we design a hi erarchical lyric generation framework that enables us to generate pleasant ly-singable lyrics without training on melody-lyric aligned data\, and sho w that our approach is competitive with strong baselines supervised on par allel data. (3) Create with Alexa: a multimodal story creation experience recently launched on Alexa devices\, which leverages story text generation models in tandem with story visualization and background music generation models to produce multimodal stories for kids.
\nBiography< /strong>
\nAlessandra Cervone is an Applied Scientist in the Natural Understanding team at Amazon Alexa AI. Alessandra holds an MSc in Speech and Language Processing from University of Edinburgh and a PhD in CS from University of Trento (Italy). During her PhD\, Alessandra worked on comput ational models of coherence in open-domain dialogue advised by Giuseppe Ri ccardi. In the first year of the PhD\, she was the team leader of one of t he teams selected to compete in the first edition of the Alexa Prize. More recently\, her research interests have been focused on natural language g eneration and its evaluation\, in particular in the context of creative AI applications.
\n \\nAbstr act
\nDespite many recent advances in automatic speech reco gnition (ASR)\, linguists and language communities engaged in language doc umentation projects continue to face the obstacle of the “transcription bo ttleneck”. Researchers in NLP typically do not distinguish between widely spoken languages that currently happen to have few training resources and endangered languages that will never have abundant data. As a result\, we often fail to thoroughly explore when ASR is helpful for language document ation\, what architectures work best for the sorts of languages that are i n need of documentation\, and how data can be collected and organized to p roduce optimal results. In this talk I describe several projects that atte mpt to bridge the gap between the promise of ASR for language documentatio n and the reality of using this technology in real-world settings.
\nBiography
\nAbstr act
\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.
\n< strong>Biography
\nLei Li is an Assistant Professor in Lang uage Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recip ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m achine translation system VolcTrans and AI writing system Xiaomingbot\, se rving one billion users.
\n X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23886@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe arms race to build increasingly larger\, powerful language models (LMs) in the past year has been remarkable. Yet incorpora ting LMs effectively into practical applications that facilitate manual wo rkflows remains challenging. I will discuss LMs’ limiting factors and our efforts to overcome them. I will start with challenges surrounding efficie nt and robust LM alignment. I will share insights from our recent paper “S elf-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for align ing itself\, an approach that has yielded some success. Then\, I will move on to the challenge of tracing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our recen t approach of ‘according-to’ prompting\, which steers LMs to quote directl y from sources observed in its pre-training. If time permits\, I will disc uss our ongoing project to adapt LMs to interact with web pages. Throughou t the presentation\, I will highlight our progress\, and end with question s about our future progress.\nBiography\nDaniel Khashabi is an assistant p rofessor in computer science at Johns Hopkins University and the Center fo r Language and Speech Processing (CLSP) member. He is interested in buildi ng reasoning-driven modular NLP systems that are robust\, transparent\, an d communicative\, particularly those that use natural language as the comm unication medium. Khashabi has published over 40 papers on natural languag e processing and AI in top-tier venues. His work touches upon developing. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Bes t Paper Award\, research gifts from the Allen Institute for AI\, and an Am azon Research Award 2023. Before joining Hopkins\, he was a postdoctoral f ellow at the Allen Institute for AI (2019-2022) and obtained a Ph.D. from the University of Pennsylvania in 2019. DTSTART;TZID=America/New_York:20230908T120000 DTEND;TZID=America/New_York:20230908T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Johns Hopkins University) “Building More Helpful L anguage Models” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-johns-hopkins-universit y/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe arms race to build increasingly larger\, powerful language models (LMs) in the past year has been remarkable. Yet incorpora ting LMs effectively into practical applications that facilitate manual wo rkflows remains challenging. I will discuss LMs’ limiting factors and our efforts to overcome them. I will start with challenges surrounding efficie nt and robust LM alignment. I will share insights from our recent paper “Self-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for aligning itself\, an approach that has yielded some success. Then\, I will move on to the challenge of t racing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our recent approach of ‘according-to’ prompting\, which steers LM s to quote directly from sources observed in its pre-training. If time per mits\, I will discuss our ongoing project to adapt LMs to interact with we b pages. Throughout the presentation\, I will highlight our progress\, and end with questions about our future progress.
\nBiography strong>
\nDaniel Khashabi is an assistant professor in computer science at Johns Hopkins University and the Center for Language and Speech Pr ocessing (CLSP) member. He is interested in building reasoning-driven modu lar NLP systems that are robust\, transparent\, and communicative\, partic ularly those that use natural language as the communication medium. Khasha bi has published over 40 papers on natural language processing and AI in t op-tier venues. His work touches upon developing. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Best Paper Award\, research gifts from the Allen Institute for AI\, and an Amazon Research Award 2023 . Before joining Hopkins\, he was a postdoctoral fellow at the Allen Insti tute for AI (2019-2022) and obtained a Ph.D. from the University of Pennsy lvania in 2019.
\n X-TAGS;LANGUAGE=en-US:2023\,Khashabi\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23888@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nEmbedding text sequences is a widespread requirement in modern language understanding. Existing approaches focus largely on con stant-size representations. This is problematic\, as the amount of informa tion contained in text often varies with the length of the input. We propo se a solution called Nugget\, which encodes language into a representation based on a dynamically selected subset of input tokens. These nuggets are learned through tasks like autoencoding and machine translation\, and int uitively segment language into meaningful units. We demonstrate Nugget out performs related approaches in tasks involving semantic comparison. Finall y\, we illustrate these compact units allow for expanding the contextual w indow of a language model (LM)\, suggesting new future LMs that can condit ion on significantly larger amounts of content. DTSTART;TZID=America/New_York:20230911T120000 DTEND;TZID=America/New_York:20230911T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Guanghui Qin “Nugget: Neural Agglomerative Embedd ings of Text (ICML 2023)” URL:https://www.clsp.jhu.edu/events/student-seminar-guanghui-qin/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nEmbedding text sequ ences is a widespread requirement in modern language understanding. Existi ng approaches focus largely on constant-size representations. This is prob lematic\, as the amount of information contained in text often varies with the length of the input. We propose a solution called Nugget\, which enco des language into a representation based on a dynamically selected subset of input tokens. These nuggets are learned through tasks like autoencoding and machine translation\, and intuitively segment language into meaningfu l units. We demonstrate Nugget outperforms related approaches in tasks inv olving semantic comparison. Finally\, we illustrate these compact units al low for expanding the contextual window of a language model (LM)\, suggest ing new future LMs that can condition on significantly larger amounts of c ontent.
\n X-TAGS;LANGUAGE=en-US:2023\,Qin\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.\nBiography\nChien-Ming Huang is the John C . Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier ven ues in HRI\, HCI\, and robotics including Science Robotics\, HRI\, CHI\, a nd CSCW. His research has received media coverage from MIT Technology Revi ew\, Tech Insider\, and Science Nation. Huang completed his postdoctoral t raining at Yale University and received his Ph.D. in Computer Science at t he University of Wisconsin–Madison. He is a recipient of the NSF CAREER aw ard. https://www.cs.jhu.edu/~cmhuang/ DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assistant Professor in the Departm ent of Computer Science at the Johns Hopkins University. His research focu ses on designing interactive AI aimed to assist and collaborate with peopl e. He publishes in top-tier venues in HRI\, HCI\, and robotics including S cience Robotics\, HRI\, CHI\, and CSCW. His research has received media co verage from MIT Technology Review\, Tech Insider\, and Science Nation. Hua ng completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www .cs.jhu.edu/~cmhuang/
\n X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23894@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.\nBiography\nMark Dredze is the John C Malone Professor of Computer Science at Johns Hopkins University and the Director of Research (Foundations of AI) for the JHU AI-X Foundry. He deve lops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.\nProf. Dredze is affiliated with the Malone Center for Engineering in Healthcare\, the Cent er for Language and Speech Processing\, among others. He holds a joint app ointment in the Biomedical Informatics & Data Science Section (BIDS)\, und er the Department of Medicine (DOM)\, Division of General Internal Medicin e (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009. DTSTART;TZID=America/New_York:20230918T120000 DTEND;TZID=America/New_York:20230918T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Dredze (Johns Hopkins University) “BloombergGPT: A Large Langu age Model for Finance” URL:https://www.clsp.jhu.edu/events/mark-dredze-johns-hopkins-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.
\nBiography
\nMark Dredze is the John C Malone Professor of Computer Science at Jo hns Hopkins University and the Director of Research (Foundations of AI) fo r the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.
\nProf. Dredze is affiliated with the Malone Center fo r Engineering in Healthcare\, the Center for Language and Speech Processin g\, among others. He holds a joint appointment in the Bio medical Informatics & Data Science Section (< span class='il'>BIDS)\, under the Department of Medicine (DOM)\, Di vision of General Internal Medicine (GIM) in the School of Medicine. He ob tained his PhD from the University of Pennsylvania in 2009.
\n HTML> X-TAGS;LANGUAGE=en-US:2023\,Dredze\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23983@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nVisually rich documents (scanned or digital) remain i mportant for many consumer and business use cases. During this talk we wil l share recent work from our team in the Document Intelligence Lab of Adob e Research to understand\, create\, and interact with these documents. Fi rst\, we’ll share a series of work on building models to decompose and und erstand the structure of documents to support use cases around document an alysis and accessibility. Next\, we’ll explore document semantic understan ding for a project where we convert natural language contract clauses to c ode to support business automation. Finally\, we’ll discuss DocEdit\, a mo del and dataset that enables editing structured documents from natural lan guage. \nBIOS:\nRajiv Jain is a Senior Research Scientist in the Document Intelligence Lab in Adobe Research\, where his research focuses on underst anding the layout\, content\, and interaction with documents. Prior to joi ning Adobe\, Rajiv was a consultant at DARPA\, where he worked on the Medi a Forensics Program to secure digital imagery. He previously served for 10 years as a researcher for the Department of Defense where he worked on pr ojects around large scale systems\, computer vision\, and network security . He received his PhD in computer science from the University of Maryland\ , College Park working in the field of document image analysis and retriev al.\nChris Tensmeyer primarily focuses on multi-modal document layout and content understanding as a Research Scientist in the Document Intelligence Lab of Adobe Research. Since joining Adobe 5 years ago\, his work has di rectly impacted popular Adobe features such as mobile Acrobat Liquid Mode\ , PDF table extraction\, handwriting recognition\, and scanned document de tection. Other research interests include general Computer Vision and Dee p Learning. He received his PhD in Computer Science from Brigham Young Un iversity on the topic of Deep Learning for Document Image Analysis. DTSTART;TZID=America/New_York:20230922T120000 DTEND;TZID=America/New_York:20230922T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rajiv Jain and Chris Tensmeyer (Adobe) “Document Intelligence at Ad obe Research” URL:https://www.clsp.jhu.edu/events/rajiv-jain-and-chris-tensmeyer-adobe-do cument-intelligence-at-adobe-research/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nVisually rich document s (scanned or digital) remain important for many consumer and business use cases. During this talk we will sha re recent work from our team in the Document Intelligence Lab of Adobe Res earch to understand\, create\, and interact with these documents. First\, we’ll share a series of work on building models to decompose and understa nd the structure of documents to support use cases around document analysi s and accessibility. Next\, we’ll explore document semantic understanding for a project where we convert natural language contract clauses to code t o support business automation. Finally\, we’ll discuss DocEdit\, a model a nd dataset that enables editing structured documents from natural language .
\nBIOS:
\nRajiv Jain is a Senior Research Scientist in the Do cument Intelligence Lab in Adobe Research\, where his research focuses on understanding the layout\, content\, and interaction with documents. Prior to joining Adobe\, Rajiv was a consultant at DARPA\, where he worked on t he Media Forensics Program to secure digital imagery. He previously served for 10 years as a researcher for the Department of Defense where he worke d on projects around large scale systems\, computer vision\, and network s ecurity. He received his PhD in computer science from the University of Ma ryland\, College Park working in the field of document image analysis and retrieval.
\nChris Ten smeyer primarily focuses on multi-modal document layout and conte nt understanding as a Research Scientist in the Document Intelligence Lab of Adobe Research. Since joining Adobe 5 years ago\, his work has directl y impacted popular Adobe features such as mobile Acrobat Liquid Mode\, PDF table extraction\, handwriting recognition\, and scanned document detecti on. Other research interests include general Computer Vision and Deep Lea rning. He received his PhD in Computer Science from Brigham Young Univers ity on the topic of Deep Learning for Document Image Analysis.
\n X-TAGS;LANGUAGE=en-US:2023\,Jain and Tensmeyer\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23896@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe field of NLP is in the midst of a disruptive shif t\, fueled most recently by the advent of large language models (LLMs)\, w ith impacts on our methodologies\, funding and public perception. While th e core technologies and scope of real-world impact of our field may be cha nging (everything is different!)\, many of the same key challenges faced s ince the inception of our field remain (nothing has changed). In this talk I’ll describe recent work characterizing and tackling some of these chall enges\, notably: data-efficient domain adaptation and lifelong learning. I will also anchor discussion of cycles and shifts in the field by describi ng findings from a qualitative study of factors shaping the community over time\, including culture\, incentives\, and infrastructure. Through these complementary lenses into the past\, present and future\, I aim to inspir e shared hope\, excitement and discussion. \nBio\nEmma Strubell is the Raj Reddy Assistant Professor in the Language Technologies Institute in the S chool of Computer Science at Carnegie Mellon University\, and a Visiting S cientist at the Allen Institute for Artificial Intelligence. Previously sh e held research scientist roles at Google and FAIR after earning her docto ral degree in 2019 from the University of Massachusetts Amherst. Her resea rch lies at the intersection of natural language processing and machine le arning\, with a focus on providing pragmatic solutions to practitioners wh o wish to gain insights from natural language text via computation- and da ta-efficient AI. Her work has been recognized with a Madrona AI Impact Awa rd\, best paper awards at ACL and EMNLP\, and cited in news outlets includ ing the New York Times and Wall Street Journal. DTSTART;TZID=America/New_York:20230925T120000 DTEND;TZID=America/New_York:20230925T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Emma Strubell (Carnegie Mellon University) “Large Language Models: Everything’s Different and Nothing Has Changed” URL:https://www.clsp.jhu.edu/events/emma-strubell-carnegie-mellon-universit y/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe field of NLP i s in the midst of a disruptive shift\, fueled most recently by the advent of large language models (LLMs)\, with impacts on our methodologies\, fund ing and public perception. While the core technologies and scope of real-w orld impact of our field may be changing (everything is different!)\, many of the same key challenges faced since the inception of our field remain (nothing has changed). In this talk I’ll describe recent work characterizi ng and tackling some of these challenges\, notably: data-efficient domain adaptation and lifelong learning. I will also anchor discussion of cycles and shifts in the field by describing findings from a qualitative study of factors shaping the community over time\, including culture\, incentives\ , and infrastructure. Through these complementary lenses into the past\, p resent and future\, I aim to inspire shared hope\, excitement and discussi on.
\nBio
\n< span class='x_x_x_ContentPasted1'>Emma Strubell is the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Compute r Science at Carnegie Mellon University\, and a Visiting Scientist at the Allen Institute for Artificial Intelligence. Previously she held research scientist roles at Google and FAIR after earning her doctoral degree in 20 19 from the University of Massachusetts Amherst. Her research lies at the intersection of natural language processing and machine learning\, with a focus on providing pragmatic solutions to practitioners who wish to gain i nsights from natural language text via computation- and data-efficient AI. Her work has been recognized with a Madrona AI Impact Award\, best paper awards at ACL and EMNLP\, and cited in news outlets including the New York Times and Wall Street Journal.
\n X-TAGS;LANGUAGE=en-US:2023\,September\,Strubell END:VEVENT BEGIN:VEVENT UID:ai1ec-23898@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nAny valuable NLP dataset has traditionally been shipp ed with crowdsourced categorical labels. Instructions for collecting these labels are easy to communicate and the labels themselves are easy to anno tate. However\, as self-supervision based methods are getting better at ba sically everything\, human annotations may need to provide more nuanced su pervision or enable more detailed evaluation in order to be worth further collecting. One natural extension to existing categorical annotation schem es is to obtain uncertainty information beyond a single hard label. In thi s talk\, I will discuss my recent efforts on introducing scalar labels in place of categorical labels as a form of uncertainty annotation. We demons trate that\, compared to other more obvious annotation schemes for eliciti ng uncertainty information\, scalar labels are significantly more cost-eff ective to annotate\, provide reliable evaluation\, and have a theoretical connection to existing predictive uncertainty metrics. In particular\, the y motivate using other losses as surrogates for calibration evaluation. DTSTART;TZID=America/New_York:20230929T120000 DTEND;TZID=America/New_York:20230929T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Zhengping Jiang “Scalar Labels for Capturing Human Uncertainty” URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-zhengping-jiang/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAny valuable NLP d ataset has traditionally been shipped with crowdsourced categorical labels . Instructions for collecting these labels are easy to communicate and the labels themselves are easy to annotate. However\, as self-supervision bas ed methods are getting better at basically everything\, human annotations may need to provide more nuanced supervision or enable more detailed evalu ation in order to be worth further collecting. One natural extension to ex isting categorical annotation schemes is to obtain uncertainty information beyond a single hard label. In this talk\, I will discuss my recent effor ts on introducing scalar labels in place of categorical labels as a form o f uncertainty annotation. We demonstrate that\, compared to other more obv ious annotation schemes for eliciting uncertainty information\, scalar lab els are significantly more cost-effective to annotate\, provide reliable e valuation\, and have a theoretical connection to existing predictive uncer tainty metrics. In particular\, they motivate using other losses as surrog ates for calibration evaluation.
\n X-TAGS;LANGUAGE=en-US:2023\,Jiang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24459@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240301T120000 DTEND;TZID=America/New_York:20240301T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mohit Iyyer “Improving\, Evaluating and Detecting Long-Form LLM-Gen erated Text” URL:https://www.clsp.jhu.edu/events/mohit-iyyer-improving-evaluating-and-de tecting-long-form-llm-generated-text/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,Iyyer\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-24461@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nMost machine translation systems operate on the sente nce-level while humans write and translate within a given context. Operati ng on individual sentences forces error-prone sentence segmentation into t he machine translation pipeline. This limits the upper-bound performance o f these systems by creating noisy training bitext. Further\, many grammati cal features necessitate inter-sentential context in order to translate wh ich makes perfect sentence-level machine translation an impossible task. I n this talk\, we will cover the inherent limits of sentence-level machine translation. Following this\, we will explore a key obstacle in the way of true context-aware machine translation—an abject lack of data. Finally\, we will cover recent work that provides (1) a new evaluation dataset that specifically addresses the translation of context-dependent discourse phe nomena and (2) reconstructed documents from large-scale sentence-level bit ext that can be used to improve performance when translating these types o f phenomena. DTSTART;TZID=America/New_York:20240304T120000 DTEND;TZID=America/New_York:20240304T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Wicks (JHU) “To Sentences and Beyond: Paving the Way for Con text-Aware Machine Translation” URL:https://www.clsp.jhu.edu/events/rachel-wicks-jhu/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nMost machine translation systems operate on the sente nce-level while humans write and translate within a given context. Operati ng on individual sentences forces error-prone sentence segmentation into t he machine translation pipeline. This limits the upper-bound performance o f these systems by creating noisy training bitext. Further\, many grammati cal features necessitate inter-sentential context in order to translate wh ich makes perfect sentence-level machine translation an impossible task. I n this talk\, we will cover the inherent limits of sentence-level machine translation. Following this\, we will explore a key obstacle in the way of true context-aware machine translation—an abject lack of data. Finally\, we will cover recent work that provides (1) a new evaluation dataset that specifically addresses the translation of context-dependent discourse phe nomena and (2) reconstructed documents from large-scale sentence-level bit ext that can be used to improve performance when translating these types o f phenomena.
\n X-TAGS;LANGUAGE=en-US:2024\,March\,Wicks END:VEVENT BEGIN:VEVENT UID:ai1ec-24465@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge Language Models (LLMs) have demonstrated remark able capabilities across various domains. However\, it is still very chall enging to build highly-reliable applications with LLMs that support specia lized use cases. LLMs trained on web data often excel at capturing general language patterns\, but they could struggle to support specialized domain s and personalized user needs. Moreover\, LLMs can produce errors that are deceptively plausible\, making them potentially dangerous for high-trust scenarios. In this talk\, I will discuss some of our recent efforts in add ressing these challenges with data-efficient tuning methods and a novel fa ctuality evaluation framework. Specifically\, my talk will focus on buildi ng multilingual applications\, one crucial use case often characterized by limited tuning and evaluation data.\nBio\nXinyi(Cindy) Wang is a research scientist at Google DeepMind working on Large Language Models(LLM) and it s application to generative question-answering. She has worked on multilin gual instruction-tuning for Gemini and multilingual generative models used in Google search. Before Google DeepMind\, Cindy Wang obtained her PhD de gree in Language Technologies at Carnegie Mellon University. During her Ph D\, she mainly worked on developing data-efficient natural language proces sing~(NLP) systems. She has made several contributions in data selection\, data representation\, and model adaptation for multilingual NLP. DTSTART;TZID=America/New_York:20240308T120000 DTEND;TZID=America/New_York:20240308T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Cindy Wang (Google DeepMind) “Building Data-Efficient and Reliable Applications with Large Language Models” URL:https://www.clsp.jhu.edu/events/cindy-wang-google-deepmind-building-dat a-efficient-and-reliable-applications-with-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge Language Models (LLMs) have demonstrated remark able capabilities across various domains. However\, it is still very chall enging to build highly-reliable applications with LLMs that support specia lized use cases. LLMs trained on web data often excel at capturing general language patterns\, but they could struggle to support specialized domain s and personalized user needs. Moreover\, LLMs can produce errors that are deceptively plausible\, making them potentially dangerous for high-trust scenarios. In this talk\, I will discuss some of our recent efforts in add ressing these challenges with data-efficient tuning methods and a novel fa ctuality evaluation framework. Specifically\, my talk will focus on buildi ng multilingual applications\, one crucial use case often characterized by limited tuning and evaluation data.
\nBio
\nXinyi(Cindy) Wang is a research scientist at Google DeepMind working on La rge Language Models(LLM) and its application to generative question-answer ing. She has worked on multilingual instruction-tuning for Gemini and mult ilingual generative models used in Google search. Before Google DeepMind\, Cindy Wang obtained her PhD degree in Language Technologies at Carnegie M ellon University. During her PhD\, she mainly worked on developing data-ef ficient natural language processing~(NLP) systems. She has made several co ntributions in data selection\, data representation\, and model adaptation for multilingual NLP.
\n X-TAGS;LANGUAGE=en-US:2024\,March\,Wang END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nThe speech field is evolving to solve more challengin g scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, a nd worked with any microphone array\, in both solo and multi-talker enviro nments. Our research enhances previous multichannel speech processing effo rts in four key areas: 1) Adaptability: Contrasting traditional models con strained to certain microphone array configurations\, our encoder is unive rsally compatible. 2) MultiTask Capability: Beyond the single-task focus o f previous systems\, UniX-Encoder acts as a robust upstream model\, adeptl y extracting features for diverse tasks including ASR and speaker recognit ion. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to mod els that first beamform then process single-channels\, our encoder offers an end-to-end solution\, bypassing explicit beamforming or separation. To validate its effectiveness\, we tested the UniXEncoder on a synthetic mult i-channel dataset from the LibriSpeech corpus. Across tasks like speech re cognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend. DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings wi th multiple simultaneous talkers. Given the many types of microphone setup s out there\, we present the UniX-Encoder. It’s a universal encoder design ed for multiple tasks\, and worked with any microphone array\, in both sol o and multi-talker environments. Our research enhances previous multichann el speech processing efforts in four key areas: 1) Adaptability: Contrasti ng traditional models constrained to certain microphone array configuratio ns\, our encoder is universally compatible. 2) MultiTask Capability: Beyon d the single-task focus of previous systems\, UniX-Encoder acts as a robus t upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integr ation: In contrast to models that first beamform then process single-chann els\, our encoder offers an end-to-end solution\, bypassing explicit beamf orming or separation. To validate its effectiveness\, we tested the UniXEn coder on a synthetic multi-channel dataset from the LibriSpeech corpus. Ac ross tasks like speech recognition and speaker diarization\, our encoder c onsistently outperformed combinations like the WavLM model with the Beamfo rmIt frontend.
\n X-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-24481@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.\nBiography\nEunsol Choi is an assistant profess or in the Computer Science department at the University of Texas at Austin . Prior to UT\, she spent a year at Google AI as a visiting researcher. He r research area spans natural language processing and machine learning. Sh e is particularly interested in interpreting and reasoning about text in a dynamic real world context. She is a recipient of a Facebook research fel lowship\, Google faculty research award\, Sony faculty award\, and an outs tanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics and comp uter science from Cornell University. DTSTART;TZID=America/New_York:20240315T120000 DTEND;TZID=America/New_York:20240315T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21209 SEQUENCE:0 SUMMARY:Eunsol Choi (University of Texas at Austin) “Knowledge-Rich Languag e Systems in a Dynamic World” URL:https://www.clsp.jhu.edu/events/eunsol-choi-university-of-texas-at-aust in-knowledge-rich-language-systems-in-a-dynamic-world/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.
\nBiography
\nEunsol Choi is an assistant professor in the Computer Scie nce department at the University of Texas at Austin. Prior to UT\, she spe nt a year at Google AI as a visiting researcher. Her research area spans n atural language processing and machine learning. She is particularly inter ested in interpreting and reasoning about text in a dynamic real world con text. She is a recipient of a Facebook research fellowship\, Google facult y research award\, Sony faculty award\, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from Unive rsity of Washington and B.A in mathematics and computer science from Corne ll University.
\n\n X-TAGS;LANGUAGE=en-US:2024\,Choi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-24489@www.clsp.jhu.edu DTSTAMP:20240329T112828Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nOver the past decade\, the field of Speech Generation has seen significant progress in enhancing speech quality and naturalness . Despite these advancements\, persistent challenges such as speech noise\ , limited high-quality data availability\, and the lack of robustness in s peech generation systems remain. Additionally\, the evaluation of speech p resents a significant obstacle for comprehensive assessment at scale. Conc urrently\, recent breakthroughs in Large Language Models (LLMs) have revol utionized text generation and natural language processing. However\, the c omplexity of spoken language introduces unique hurdles\, including managin g long speech waveform sequences. In this presentation\, I will explore re cent innovations in speech synthesis with spoken language modeling\, evalu ation for generative speech systems and high-fidelity speech enhancement. Finally\, I will discuss prospective avenues for future research aimed at addressing these challenges.\nBio\nSoumi Maiti is a postdoctoral researche r at Language Technologies Institute\, Carnegie Mellon University\, where she works on speech and language processing. Her research broadly focuses on building intelligent systems that can communicate with humans naturally . She earned a Ph.D. from the Graduate Center\, City University of New Yo rk (CUNY) with the Graduate Center Fellowship advised by Prof Michael Mand el. She earned her B.Tech. in Computer Science from the Indian Institute o f Engineering Science and Technology\, Shibpur. Previously\, she has worke d in the Text-To-Speech team at Apple. She has also worked at Google and I nteractions LLC as a student researcher and research intern. She has worke d as an adjunct lecturer at Brooklyn College\, CUNY\, for three years and served as a Math Fellow at Hunter College. She has served as session chair in ICASSP 2024\, ICASSP 2023\, SLT 2023 and others\, and area chair at EM NLP 2023.\n DTSTART;TZID=America/New_York:20240329T120000 DTEND;TZID=America/New_York:20240329T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Soumi Maiti (CMU) “Towards Robust Speech Generation” URL:https://www.clsp.jhu.edu/events/soumi-maiti/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nOver the past decade\, the field of Speech Generation has seen significant progress in enhancing speech quality and naturalness . Despite these advancements\, persistent challenges such as speech noise\ , limited high-quality data availability\, and the lack of robustness in s peech generation systems remain. Additionally\, the evaluation of speech p resents a significant obstacle for comprehensive assessment at scale. Conc urrently\, recent breakthroughs in Large Language Models (LLMs) have revol utionized text generation and natural language processing. However\, the c omplexity of spoken language introduces unique hurdles\, including managin g long speech waveform sequences. In this presentation\, I will explore re cent innovations in speech synthesis with spoken language modeling\, evalu ation for generative speech systems and high-fidelity speech enhancement. Finally\, I will discuss prospective avenues for future research aimed at addressing these challenges.
\nBio
\nSoumi Ma iti is a postdoctoral researcher at Language Technologies Institute\, Carn egie Mellon University\, where she works on speech and language processing . Her research broadly focuses on building intelligent systems that can co mmunicate with humans naturally. She earned a Ph.D. from the Graduate Cen ter\, City University of New York (CUNY) with the Graduate Center Fellowsh ip advised by Prof Michael Mandel. She earned her B.Tech. in Computer Scie nce from the Indian Institute of Engineering Science and Technology\, Shib pur. Previously\, she has worked in the Text-To-Speech team at Apple. She has also worked at Google and Interactions LLC as a student researcher and research intern. She has worked as an adjunct lecturer at Brooklyn Colleg e\, CUNY\, for three years and served as a Math Fellow at Hunter College. She has served as session chair in ICASSP 2024\, ICASSP 2023\, SLT 2023 an d others\, and area chair at EMNLP 2023.
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