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-21068@www.clsp.jhu.edu DTSTAMP:20240329T075951Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20211203T120000 DTEND;TZID=America/New_York:20211203T131500 LOCATION:Hackerman HallB17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Eric Ringger (Zillow Group) URL:https://www.clsp.jhu.edu/events/eric-ringger-zillow-group/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,December\,Ringger END:VEVENT BEGIN:VEVENT UID:ai1ec-21267@www.clsp.jhu.edu DTSTAMP:20240329T075951Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn this talk\, I present a multipronged strategy for zero-shot cross-lingual Information Extraction\, that is the construction of an IE model for some target language\, given existing annotations exclu sively in some other language. This work is part of the JHU team’s effort under the IARPA BETTER program. I explore data augmentation techniques inc luding data projection and self-training\, and how different pretrained en coders impact them. We find through extensive experiments and extension of techniques that a combination of approaches\, both new and old\, leads to better performance than any one cross-lingual strategy in particular.\nBi ography\nMahsa Yarmohammadi is an assistant research scientist in CLSP\, J HU\, who leads state-of-the-art research in cross-lingual language and spe ech applications and algorithms. A primary focus of Yarmohammadi’s researc h is using deep learning techniques to transfer existing resources into ot her languages and to learn representations of language from multilingual d ata. She also works in automatic speech recognition and speech translation . Yarmohammadi received her PhD in computer science and engineering from O regon Health & Science University (2016). She joined CLSP as a post-doctor al fellow in 2017. DTSTART;TZID=America/New_York:20220204T120000 DTEND;TZID=America/New_York:20220204T131500 LOCATION:Ames 234 Presented Virtually via Zoom https://wse.zoom.us/j/967351 83473 SEQUENCE:0 SUMMARY:Mahsa Yarmohammadi (Johns Hopkins University) “Data Augmentation fo r Zero-shot Cross-Lingual Information Extraction” URL:https://www.clsp.jhu.edu/events/mahsa-yarmohammadi-johns-hopkins-univer sity-data-augmentation-for-zero-shot-cross-lingual-information-extraction/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nIn this talk\, I present a multipronged strategy for zero-shot cross-lingual Information Extraction\, that is the construction of an IE model for some target language\, given existing annotations exclu sively in some other language. This work is part of the JHU team’s effort under the IARPA BETTER program. I explore data augmentation techniques inc luding data projection and self-training\, and how different pretrained en coders impact them. We find through extensive experiments and extension of techniques that a combination of approaches\, both new and old\, leads to better performance than any one cross-lingual strategy in particular.
\nBiography
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\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.
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