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UID:/NewsandEvents/Archives/2022/newsitem/13971/13
 -December-2022-Computational-Linguistics-Seminar-T
 ejaswini-Deoskar
DTSTAMP:20221208T163731
SUMMARY:Computational Linguistics Seminar, Tejaswi
 ni Deoskar
ATTENDEE;ROLE=Speaker:Tejaswini Deoskar (Utrecht U
 niversity)
DTSTART;TZID=Europe/Amsterdam:20221213T160000
LOCATION:Room L3.36 at LAB42, Amsterdam Science Pa
 rk, plus live streaming on Zoom.
DESCRIPTION:In this talk, I will discuss ongoing r
 esearch on several topics, on the general theme of
  generalisation in natural language models. First,
  I will talk about the generalisation problem in a
 nalytically complex syntactic parsers, where it is
  necessary to go beyond supervised models, for ins
 tance for parsing out-of-domain data or low-resour
 ce languages; specifically I will present recent r
 esults on constructing complex category types (in 
 CCG or other categorial grammars) that are unseen 
 in the training data, on-the-fly. Second, I will d
 iscuss a use-case for a syntactic parser applied t
 o a new domain: detecting syntactic markers of “ag
 ency” in language use. Loss of agency is correlate
 d to psychological conditions like depression or f
 atigue syndrome, and often expressed in the langua
 ge produced by patients (e.g. in excessive use of 
 passives). Automatic detection of such markers can
  help medical professionals intervene and predict 
 recovery in online treatments. Third, I will discu
 ss recent research on incorporating image-external
  knowledge for “contextualised” image-captioning: 
 here we develop a generalisable system that can id
 entify broad-coverage external knowledge relevant 
 to an image. The system can generate informative a
 s well as factually-correct captions, and be appli
 ed to various image-language scenarios.
X-ALT-DESC;FMTTYPE=text/html:\n  <p>In this talk, 
 I will discuss ongoing research on several topics,
  on the general theme of generalisation in natural
  language models. First, I will talk about the gen
 eralisation problem in analytically complex syntac
 tic parsers, where it is necessary to go beyond su
 pervised models, for instance for parsing out-of-d
 omain data or low-resource languages; specifically
  I will present recent results on constructing com
 plex category types (in CCG or other categorial gr
 ammars) that are unseen in the training data, on-t
 he-fly. Second, I will discuss a use-case for a sy
 ntactic parser applied to a new domain: detecting 
 syntactic markers of “agency” in language use. Los
 s of agency is correlated to psychological conditi
 ons like depression or fatigue syndrome, and often
  expressed in the language produced by patients (e
 .g. in excessive use of passives). Automatic detec
 tion of such markers can help medical professional
 s intervene and predict recovery in online treatme
 nts. Third, I will discuss recent research on inco
 rporating image-external knowledge for “contextual
 ised” image-captioning: here we develop a generali
 sable system that can identify broad-coverage exte
 rnal knowledge relevant to an image. The system ca
 n generate informative as well as factually-correc
 t captions, and be applied to various image-langua
 ge scenarios.</p>\n
URL:https://projects.illc.uva.nl/LaCo/CLS/
CONTACT:Alina Leidinger at a.j.leidinger at uva.nl
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