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BEGIN:VEVENT
UID:/NewsandEvents/Archives/2019/newsitem/10539/1-
 February-2019-Cool-Logic-Jack-Harding
DTSTAMP:20190128T150101
SUMMARY:Cool Logic, Jack Harding
ATTENDEE;ROLE=Speaker:Jack Harding
DTSTART;TZID=Europe/Amsterdam:20190201T180000
DTEND;TZID=Europe/Amsterdam:20190201T190000
LOCATION:ILLC seminar room F1.15, Science Park 107
 , Amsterdam
DESCRIPTION:How do neural language models keep tra
 ck of number agreement between subject and verb? W
 e show that `diagnostic classifiers', trained to p
 redict number from the internal states of a langua
 ge model, provide a detailed understanding of how,
  when, and where this information is represented. 
 Moreover, they give us insight into when and where
  number information is corrupted in cases where th
 e language model ends up making agreement errors. 
 To demonstrate the causal role played by the repre
 sentations we find, we then use agreement informat
 ion to influence the course of the LSTM during the
  processing of difficult sentences. Results from s
 uch an intervention reveal a large increase in the
  language model's accuracy. Together, these result
 s show that diagnostic classifiers give us an unri
 valled detailed look into the representation of li
 nguistic information in neural models, and demonst
 rate that this knowledge can be used to improve th
 eir performance.As always, after the talk there wi
 ll be beers and snack in the common room.
X-ALT-DESC;FMTTYPE=text/html:\n  <p>How do neural 
 language models keep track of number agreement bet
 ween subject and verb? We show that `diagnostic cl
 assifiers', trained to predict number from the int
 ernal states of a language model, provide a detail
 ed understanding of how, when, and where this info
 rmation is represented. Moreover, they give us ins
 ight into when and where number information is cor
 rupted in cases where the language model ends up m
 aking agreement errors. To demonstrate the causal 
 role played by the representations we find, we the
 n use agreement information to influence the cours
 e of the LSTM during the processing of difficult s
 entences. Results from such an intervention reveal
  a large increase in the language model's accuracy
 . Together, these results show that diagnostic cla
 ssifiers give us an unrivalled detailed look into 
 the representation of linguistic information in ne
 ural models, and demonstrate that this knowledge c
 an be used to improve their performance.As always,
  after the talk there will be beers and snack in t
 he common room.</p>\n
URL:https://events.illc.uva.nl/coollogic/talks/98
CONTACT:Rachael Colley at rachaelhcolley at gmail.
 com
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