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-21057@www.clsp.jhu.edu DTSTAMP:20240329T150042Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nThis talk will outline the major challenging in porting mainstream speech technology to the domain o f clinical applications\; in particular\, the need for personalised system s\, the challenge of working in an inherently sparse data domain and devel oping meaningful collaborations with all stakeholders. The talk will give an overview of recent state-of-the-art research from current projects incl uding in the areas of recognition of disordered speech\, automatic process ing of conversations and the automatic detection and tracking of paralingu istic information at the University of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab.
\nBiography
\nHei di is a Senior Lecturer (associate professor) in Computer Science at the U niversity of Sheffield\, United Kingdom. Her research interests are on the application of AI-based voice technologies to healthcare. In particular\, the detection and monitoring of people’s physical and mental health inclu ding verbal and non-verbal traits for expressions of emotion\, anxiety\, d epression and neurodegenerative conditions in e.g.\, therapeutic or diagno stic settings.
DTSTART;TZID=America/New_York:20211119T120000 DTEND;TZID=America/New_York:20211119T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Heidi Christensen (University of Sheffield\, UK) Virtual Seminar “A utomated Processing of Pathological Speech: Recent Work and Ongoing Challe nges” URL:https://www.clsp.jhu.edu/events/heidi-christensen-university-of-sheffie ld-uk-virtual-seminar-automated-processing-of-pathological-speech-recent-w ork-and-ongoing-challenges/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,Christensen\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-22374@www.clsp.jhu.edu DTSTAMP:20240329T150042Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nIn recent years\, the fiel d of Natural Language Processing has seen a profusion of tasks\, datasets\ , and systems that facilitate reasoning about real-world situations throug h language (e.g.\, RTE\, MNLI\, COMET). Such systems might\, for example\, be trained to consider a situation where “somebody dropped a glass on the floor\,” and conclude it is likely that “the glass shattered” as a result . In this talk\, I will discuss three pieces of work that revisit assumpti ons made by or about these systems. In the first work\, I develop a Defeas ible Inference task\, which enables a system to recognize when a prior ass umption it has made may no longer be true in light of new evidence it rece ives. The second work I will discuss revisits partial-input baselines\, wh ich have highlighted issues of spurious correlations in natural language r easoning datasets and led to unfavorable assumptions about models’ reasoni ng abilities. In particular\, I will discuss experiments that show models may still learn to reason in the presence of spurious dataset artifacts. F inally\, I will touch on work analyzing harmful assumptions made by reason ing models in the form of social stereotypes\, particularly in the case of free-form generative reasoning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Co mputer Science at the University of Maryland\, College Park. She holds joi nt appointments in the Department of Linguistics and the Institute for Adv anced Computer Studies (UMIACS). In 2019\, Rachel completed her Ph.D. in C omputer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Investigator at the A llen Institute for AI in Seattle\, and a visiting researcher at the Univer sity of Washington. Her research interests include computational 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-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23320@www.clsp.jhu.edu DTSTAMP:20240329T150042Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nSpeech communications repr esents a core domain for education\, team problem solving\, social engagem ent\, and business interactions. The ability for Speech Technology to extr act layers of knowledge and assess engagement content represents the next generation of advanced speech solutions. Today\, the emergence of BIG DATA \, Machine Learning\, as well as voice enabled speech systems have require d the need for effective voice capture and automatic speech/speaker recogn ition. The ability to employ speech and language technology to assess huma n-to-human interactions offers new research paradigms having profound impa ct on assessing human interaction. In this talk\, we will focus on big dat a naturalistic audio processing relating to (i) child learning spaces\, an d (ii) the NASA APOLLO lunar missions. ML based technology advancements in clude automatic audio diarization\, speech recognition\, and speaker recog nition. Child-Teacher based assessment of conversational interactions are explored\, including keyword and “WH-word” (e.g.\, who\, what\, etc.). Dia rization processing solutions are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 corpus\, resulting in a massive multi-track audio processing challenge to make available 150\,000hrs of Apollo mission data to be shared with science communities: (i) speech/language technology\, (i i) STEM/science and team-based researchers\, and (iii) education/historica l/archiving specialists. Our goals here are to provide resources which all ow to better understand how people work/learn collaboratively together. Fo r Apollo\, to accomplish one of mankind’s greatest scientific/technologica l challenges in the last century.
\nBiography
\nJohn H.L. Hansen\, received 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 2005\, where he currently serves as Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair in Telecom. Engin eering\, and directs Center for Robust Speech Systems (CRSS). He is an ISC A Fellow\, IEEE Fellow\, and has served as Member and TC-Chair of IEEE Sig nal Proc. Society\, Speech & Language Proc. Tech. Comm.(SLTC)\, and Techni cal Advisor to U.S. Delegate for NATO (IST/TG-01). He served as ISCA Presi dent (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.\,C og.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author of 865 journal/c onference papers including 14 textbooks in the field of speech/language/he aring processing & technology including coauthor of textbook Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and lead author of t he report “The Impact of Speech Under ‘Stress’ on Military Speech Technolo gy\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Chair/Co-Chair/Tec h.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISC A INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIOUS SERVICE Award .
\nDTSTART;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-TAGS;LANGUAGE=en-US:2023\,Hansen\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23894@www.clsp.jhu.edu DTSTAMP:20240329T150042Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nThe use of NLP in the real m of financial technology is broad and complex\, with applications ranging from sentiment analysis and named entity recognition to question answerin g. Large Language Models (LLMs) have been shown to be effective on a varie ty of tasks\; however\, no LLM specialized for the financial domain has be en reported in the literature. In this work\, we present BloombergGPT\, a 50 billion parameter language model that is trained on a wide range of fin ancial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, perhaps the largest domain-specific dataset yet\ , augmented with 345 billion tokens from general-purpose datasets. We val idate BloombergGPT on standard LLM benchmarks\, open financial benchmarks\ , and a suite of internal benchmarks that most accurately reflect our inte nded usage. Our mixed dataset training leads to a model that outperforms e xisting models on financial tasks by significant margins without sacrifici ng performance on general LLM benchmarks. Additionally\, we explain our mo deling choices\, training process\, and evaluation methodology.
\nBiography
Mark Dredze is the John C Malone Professo r of Computer Science at Johns Hopkins University and the Director of Rese arch (Foundations of AI) for the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores app lications to public health and medicine.
\nProf. Dredze is affiliate d with the Malone Center for Engineering in Healthcare\, the Center for La nguage and Speech Processing\, among others. He holds a joint appointment in the Biomedical Informatics & Data Science Section (BIDS)\, under the Depart ment of Medicine (DOM)\, Division of General Internal Medicine (GIM) in th e School of Medicine. He obtained his PhD from the University of Pennsylva nia 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-TAGS;LANGUAGE=en-US:2023\,Dredze\,September END:VEVENT END:VCALENDAR