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-21497@www.clsp.jhu.edu DTSTAMP:20240329T123458Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nWhile the “deep learning t sunami” continues to define the state of the art in speech and language pr ocessing\, finite-state transducer grammars developed by linguists and eng ineers are still widely used in industrial\, highly-multilingual settings\ , particularly for symbolic\, “front-end” speech applications. In this tal k\, I will first briefly review the current state of the OpenFst and OpenG rm finite-state transducer libraries. I then review two “late-breaking” al gorithms found in these libraries. The first is a heuristic but highly-eff ective general-purpose optimization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-det erministic weighted acceptors which lack certain properties required by cl assic shortest-path algorithms. I will then illustrate how the OpenGrm too ls can be used to induce a finite-state string-to-string transduction mode l known as a pair n-gram model. This model has been applied to grapheme-to -phoneme conversion\, loanword detection\, abbreviation expansion\, and ba ck-transliteration\, among other tasks.
\nBiography
\nKyle Gorman is an assistant professor of linguistics at the Gradu ate Center\, City University of New York\, and director of the master’s pr ogram in computational linguistics\; he is also a software engineer in the speech and language algorithms group at Google. With Richard Sproat\, he is the coauthor of Finite-State Text Processing (Morgan & Claypool\ , 2021) and the creator of Pynini\, a finite-state text processing library for Python. He has also published on statistical methods for comparing co mputational models\, text normalization\, grapheme-to-phoneme conversion\, and morphological analysis\, as well as many topics in linguistic 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-TAGS;LANGUAGE=en-US:2022\,Gorman\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23894@www.clsp.jhu.edu DTSTAMP:20240329T123458Z 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