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-21267@www.clsp.jhu.edu DTSTAMP:20240329T230006Z 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 exclusively in some other language. This work is par t of the JHU team’s effort under the IARPA BETTER program. I explore data augmentation techniques including data projection and self-training\, and how different pretrained encoders impact them. We find through extensive e xperiments 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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