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-20987@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.\nBiography\nI’m a research scientist at the HLTCOE at Johns Hopk ins University. My primary research interests are in language generation\, diverse and constrained decoding\, and information retrieval. During my P hD I focused mainly on the task of text simplification\, and now am workin g on formulating structured prediction problems as end-to-end generation t asks. I received my PhD in July 2021 from the University of Pennsylvania w ith Chris Callison-Burch and Marianna Apidianaki. DTSTART;TZID=America/New_York:20211022T120000 DTEND;TZID=America/New_York:20211022T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Reno Kriz (HLTCOE – JHU) “Towards a Practically Useful Text Simplif ication System” URL:https://www.clsp.jhu.edu/events/reno-kriz-hltcoe-jhu-towards-a-practica lly-useful-text-simplification-system/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-21023@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech data is notoriously difficult to work with due to a variety of codecs\, lengths of recordings\, and meta-data formats. W e present Lhotse\, a speech data representation library that draws upon le ssons learned from Kaldi speech recognition toolkit and brings its concept s into the modern deep learning ecosystem. Lhotse provides a common JSON d escription format with corresponding Python classes and data preparation r ecipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and cloud storage\, la zy and on-the-fly operations amongst other features. We introduce Cut and CutSet concepts\, which simplify common data wrangling tasks for audio and help incorporate acoustic context of speech utterances. Finally\, we show how Lhotse leverages PyTorch data API abstractions and adopts them to han dle speech data for deep learning.\nBiography\nPiotr Zelasko is an assista nt research scientist in the Center for Language and Speech Processing (CL SP) who specializes in automatic speech recognition (ASR) and spoken langu age understanding (SLU). His current research focuses on applying multilin gual and crosslingual speech recognition systems to categorize the phoneti c inventory of a previously unknown language and on improving defenses aga inst adversarial attacks on both speaker identification and automatic spee ch recognition systems. He is also addressing the question of how to struc ture a spontaneous conversation into high-level semantic units such as dia log acts or topics. Finally\, he is working on Lhotse + K2\, the next-gene ration speech processing research software ecosystem. Before joining Johns Hopkins\, Zelasko worked as a machine learning consultant for Avaya (2017 -2019)\, and as a machine learning engineer for Techmo (2015-2017). Zelask o received his PhD (2019) in electronics engineering\, as well as his mast er’s (2014) and undergraduate degrees (2013) in acoustic engineering from AGH University of Science and Technology in Kraków\, Poland. DTSTART;TZID=America/New_York:20211029T120000 DTEND;TZID=America/New_York:20211029T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore MD 21218 SEQUENCE:0 SUMMARY:Piotr Zelasko (CLSP at JHU) “Lhotse: a speech data representation l ibrary for the modern deep learning ecosystem” URL:https://www.clsp.jhu.edu/events/piotr-zelasko-clsp-at-jhu-lhotse-a-spee ch-data-representation-library-for-the-modern-deep-learning-ecosystem/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nSpeech data is notoriously difficult t o work with due to a variety of codecs\, lengths of recordings\, and meta- data formats. We present Lhotse\, a speech data representation library tha t draws upon lessons learned from Kaldi speech recognition toolkit and bri ngs its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with corresponding Python classes and dat a preparation recipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and clo ud storage\, lazy and on-the-fly operations amongst other features. We int roduce Cut and CutSet concepts\, which simplify common data wrangling task s for audio and help incorporate acoustic context of speech utterances. Fi nally\, we show how Lhotse leverages PyTorch data API abstractions and ado pts them to handle speech data for deep learning.
\nB iography
\nPiotr Zelasko is an assistant research scientist in the Center for Language and Speech Processing (CLSP) who specializes i n automatic speech recognition (ASR) and spoken language understanding (SL U). His current research focuses on applying multilingual and crosslingual speech recognition systems to categorize the phonetic inventory of a prev iously unknown language and on improving defenses against adversarial atta cks on both speaker identification and automatic speech recognition system s. He is also addressing the question of how to structure a spontaneous co nversation into high-level semantic units such as dialog acts or topics. F inally\, he is working on Lhotse + K2\, the next-generation speech process ing research software ecosystem. Before joining Johns Hopkins\, Zelasko wo rked as a machine learning consultant for Avaya (2017-2019)\, and as a mac hine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2 019) in electronics engineering\, as well as his master’s (2014) and under graduate degrees (2013) in acoustic engineering from AGH University of Sci ence and Technology in Kraków\, Poland.
\n X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-22423@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20221007T120000 DTEND;TZID=America/New_York:20221007T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ariya Rastrow (Amazon) URL:https://www.clsp.jhu.edu/events/ariya-rastrow-amazon-2/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Rastrow END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\n\nModel robustness and spurious correlations have rec eived increasing attention in the NLP community\, both in methods and eval uation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model\, as judged by domain e xperts.\n\n\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, man y features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image . In contrast\, they carry important information that’s needed to make pre dictions by humans. In this talk\, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for pred iction. We then discuss the implications of this categorization in represe ntation\, learning\, and evaluation.\nBiography\nHe He is an Assistant Pro fessor in the Department of Computer Science and the Center for Data Scien ce at New York University. She obtained her PhD in Computer Science at the University of Maryland\, College Park. Before joining NYU\, she spent a y ear at AWS AI and was a post-doc at Stanford University before that. She i s interested in building robust and trustworthy NLP systems in human-cente red settings. Her recent research focus includes robust language understan ding\, collaborative text generation\, and understanding capabilities and issues of large language models. DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
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\nModern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expre ssions. In this talk\, I will give a few examples of exploring alternativ e architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and a ccessing knowledge in a dedicated memory component.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Goo gle Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collaborators in Google Research.< /p>\n
Biography
\nFei is a research scientist at Google Research. Before that\, he was a Professor of Computer Science at U niversity of Southern California. His primary research interests are machi ne learning and its application to various AI problems: speech and languag e processing\, computer vision\, robotics and recently weather forecast an d climate modeling. He has a PhD (2007) from Computer and Information Sc ience from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
\n X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23900@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231002T120000 DTEND;TZID=America/New_York:20231002T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Anna Favaro URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-anna-favaro/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Favaro\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24115@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nOur goal is to use AI to automatically find tax minim ization strategies\, an approach which we call “Shelter Check.” It would c ome in two variants. Existing-Authority Shelter Check would aim to find wh ether existing tax law authorities can be combined to create tax minimizat ion strategies\, so the IRS or Congress can shut them down. New-Authority Shelter Check would automate checking whether a new tax law authority – li ke proposed legislation or a draft court decision – would combine with exi sting authorities to create a new tax minimization strategy. We had initia lly had high hopes for GPT-* large language models for implementing Shelte r Check\, but our tests have showed that they do very poorly at basic lega l reasoning and handling legal text. So we are now creating a benchmark an d training data for LLM’s handling legal text\, hoping to spur improvement s. DTSTART;TZID=America/New_York:20231006T120000 DTEND;TZID=America/New_York:20231006T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Andrew Blair-Stanek “Shelter Check and GPT-4 ’s Bad Legal Performance” URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-andrew-blair-stane k-shelter-check-and-gpt-4s-bad-legal-performance/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nOur goal is to use AI to automatically find tax minim ization strategies\, an approach which we call “Shelter Check.” It would c ome in two variants. Existing-Authority Shelter Check would aim to find wh ether existing tax law authorities can be combined to create tax minimizat ion strategies\, so the IRS or Congress can shut them down. New-Authority Shelter Check would automate checking whether a new tax law authority – li ke proposed legislation or a draft court decision – would combine with exi sting authorities to create a new tax minimization strategy. We had initia lly had high hopes for GPT-* large language models for implementing Shelte r Check\, but our tests have showed that they do very poorly at basic lega l reasoning and handling legal text. So we are now creating a benchmark an d training data for LLM’s handling legal text\, hoping to spur improvement s.
\n X-TAGS;LANGUAGE=en-US:2023\,Blair-Stanek\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24005@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision resear ch. These models not only generate high fidelity text or image outputs\, b ut also demonstrate impressive domain and task generalization capabilities . In contrast\, audio generative models are relatively primitive in scale and generalization.\nIn this talk\, I will start with a brief introduction on conventional neural speech generative models and discuss why they are unfit for scaling to Internet-scale data. Next\, by reviewing the latest l arge-scale generative models for text and image\, I will outline a few lin es of promising approaches to build scalable speech models. Last\, I will present Voicebox\, our latest work to advance this area. Voicebox is the m ost versatile generative model for speech. It is trained with a simple tas k — text conditioned speech infilling — on over 50K hours of multilingual speech with a powerful flow-matching objective. Through in-context learnin g\, Voicebox can perform monolingual/cross-lingual zero-shot TTS\, holisti c style conversion\, transient noise removal\, content editing\, and diver se sample generation. Moreover\, Voicebox achieves state-of-the-art perfor mance and excellent run-time efficiency.\nBiography\nWei-Ning Hsu is a res earch scientist at Meta Foundational AI Research (FAIR) and currently the lead of the audio generation team. His research focuses on self-supervised learning and generative models for speech and audio. His pioneering work includes HuBERT\, AV-HuBERT\, TextlessNLP\, data2vec\, wav2vec-U\, textles s speech translation\, and Voicebox. \nPrior to joining Meta\, Wei-Ning wo rked at MERL and Google Brain as a research intern. He received his Ph.D. and S.M. degrees in Electrical Engineering and Computer Science from Massa chusetts Institute of Technology in 2020 and 2018\, under the supervision of Dr. James Glass. He received his B.S. degree in Electrical Engineering from National Taiwan University in 2014\, under the supervision of Prof. L in-shan Lee and Prof. Hsuan-Tien Lin. DTSTART;TZID=America/New_York:20231009T120000 DTEND;TZID=America/New_York:20231009T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei-Ning Hsu (Meta Foundational AI Research) “Large Scale Universal Speech Generative Models” URL:https://www.clsp.jhu.edu/events/wei-ning-hsu-meta-foundational-ai-resea rch-large-scale-universal-speech-generative-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge-scale generative models such as GPT and DALL-E have revolutionized natural langu age processing and computer vision research. These models not only generat e high fidelity text or image outputs\, but also demonstrate impressive do main and task generalization capabilities. In contrast\, audio generative models are relatively primitive in scale and generalization.
\nIn this talk\, I will st art with a brief introduction on conventional neural speech generative mod els and discuss why they are unfit for scaling to Internet-scale data. Nex t\, by reviewing the latest large-scale generative models for text and ima ge\, I will outline a few lines of promising approaches to build scalable speech models. Last\, I will present Voicebox\, our latest work to advance this area. Voicebox is the most versatile generative model for speech. It is trained with a simple task — text conditioned speech infilling — on ov er 50K hours of multilingual speech with a powerful flow-matching objectiv e. Through in-context learning\, Voicebox can perform monolingual/cross-li ngual zero-shot TTS\, holistic style conversion\, transient noise removal\ , content editing\, and diverse sample generation. Moreover\, Voicebox ach ieves state-of-the-art performance and excellent run-time efficiency.
\nBiography
\nWei-Ning Hsu is a research scientist at Meta Founda tional AI Research (FAIR) and currently the lead of the audio generation t eam. His research focuses on self-supervised learning and generative model s for speech and audio. His pioneering work includes HuBERT\, AV-HuBERT\, TextlessNLP\, data2vec\, wav2vec-U\, textless speech translation\, and Voi cebox.
\nPri or to joining Meta\, Wei-Ning worked at MERL and Google Brain as a researc h intern. He received his Ph.D. and S.M. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2020 a nd 2018\, under the supervision of Dr. James Glass. He received his B.S. d egree in Electrical Engineering from National Taiwan University in 2014\, under the supervision of Prof. Lin-shan Lee and Prof. Hsuan-Tien Lin.
\n X-TAGS;LANGUAGE=en-US:2023\,Hsu\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23902@www.clsp.jhu.edu DTSTAMP:20240328T103429Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nPretrained language models (LMs) encode implicit repr esentations of knowledge in their parameters. Despite this observation\, o ur best methods for interpreting these representations yield few actionabl e insights on how to manipulate this parameter space for downstream benefi t. In this talk\, I will present work on methods that simulate machine rea soning by localizing and modifying parametric knowledge representations. F irst\, I will present a method for discovering knowledge-critical subnetwo rks within pretrained language models\, and show that these sparse computa tional subgraphs are responsible for the model’s ability to encode specifi c pieces of knowledge. Then\, I will present a new reasoning algorithm\, R ECKONING\, a bi-level optimisation procedure that dynamically encodes and reasons over new knowledge at test-time using the model’s existing learned knowledge representations as a scratchpad. Finally\, I will discuss next steps and challenges in using internal model mechanisms for reasoning.\n\n Bio\n\nAntoine Bosselut is an assistant professor in the School of Compute r and Communication Sciences at the École Polytechnique Fédéral de Lausann e (EPFL). He was a postdoctoral scholar at Stanford University and a Young Investigator at the Allen Institute for AI (AI2). He completed his PhD at the University of Washington and was a student researcher at Microsoft Re search. His research interests are in building systems that mix knowledge and language representations to solve problems in NLP\, specializing in co mmonsense representation and reasoning. DTSTART;TZID=America/New_York:20231013T120000 DTEND;TZID=America/New_York:20231013T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Antoine Bosselut (EPFL) “From Mechanistic Interpretability to Mecha nistic Reasoning” URL:https://www.clsp.jhu.edu/events/antoine-bosselut-epfl/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
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\nRecent advances in speech technology make heavy use o f pre-trained models that learn from large quantities of raw (untranscribe d) speech\, using “self-supervised” (ie unsupervised) learning. These mode ls learn to transform the acoustic input into a different representational format that makes supervised learning (for tasks such as transcription or even translation) much easier. However\, *what* and *how* speech-relevant information is encoded in these representations is not well understood. I will talk about some work at various stages of completion in which my gro up is analyzing the structure of these representations\, to gain a more sy stematic understanding of how word-level\, phonetic\, and speaker informat ion is encoded.
\nBiography
\nSharon Goldwate
r is a Professor in the Institute for Language\, Cognition and Computation
at the University of Edinburgh’s School of Informatics. She received her
PhD in 2007 from Brown University and spent two years as a postdoctoral re
searcher at Stanford University before moving to Edinburgh. Her research i
nterests include unsupervised and minimally-supervised learning for speech
and language processing\, computer modelling of language acquisition in c
hildren\, and computational studies of language use. Her main focus withi
n linguistics has been on the lower levels of structure including phonetic
s\, phonology\, and morphology.
Prof. Goldwater has received awards including the 2016 Roger Needha
m Award from the British Computer Society for “distinguished research cont
ribution in computer science by a UK-based researcher who has completed up
to 10 years of post-doctoral research.” She has served on the editorial b
oards of several journals\, including Computational Linguistics\, Transact
ions of the Association for Computational Linguistics\, and the inaugural
board of OPEN MIND: Advances in Cognitive Science. She was a program chair
for the EACL 2014 Conference and chaired the EACL governing board from 20
19-2020.