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SUMMARY:Interpretability in NLP: Moving Beyond Vision - Shuoyang Ding (Joh
 ns Hopkins University)
DTSTART:20210528T120000Z
DTEND:20210528T130000Z
UID:TALK160306@talks.cam.ac.uk
CONTACT:Huiyuan Xie
DESCRIPTION:Join Zoom Meeting\nhttps://cl-cam-ac-uk.zoom.us/j/92766937414?
 pwd=bHJ1TDRqbHRHN0l0eEdPUkxHNlVYQT09\n\nMeeting ID: 927 6693 7414\nPasscod
 e: 751190\n\nDeep neural network models have been extremely successful for
  natural language processing (NLP) applications in recent years\, but one 
 complaint they often suffer from is their lack of interpretability. On the
  other hand\, the field of computer vision has navigated their own way of 
 improving interpretability for deep learning models\, most notably with po
 st-hoc interpretation methods such as saliency. In this talk\, we investig
 ate the possibility of deploying these interpretation methods to natural l
 anguage processing applications. Our study covers common NLP applications 
 such as language modeling and neural machine translation\, and we stress t
 he necessity of quantitative evaluations of interpretations apart from qua
 litative evaluations. We show that this adaptation is feasible at least in
  some scenarios\, while also pointing out some shortcomings of the current
  practice that may shed light on future research directions.
LOCATION:Virtual (Zoom)
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