2018년 제4차 언어학 콜로퀴엄(Sam Bowman)

2018년 제4차 언어학 콜로퀴엄

- 일시: 2018. 9. 10.(월) 오후 5시
- 장소: 신양인문관 국제회의실(4동 302호)
- 발표자: Sam Bowman (New York Univ.)
- 제목: Evaluating the Semantic and Syntactic Abilities of Neural Network Models
- 초록: Artificial neural network models for language understanding problems represent an increasingly large and increasingly successful thread of research within natural language processing. When developing these models in typical settings, though, it can be difficult to identify the degree to which they capture the structures or meanings of natural language sentences, and correspondingly difficult to identify research directions that are likely to yield progress on the underlying language understanding problem.
In this talk, I first introduce natural language inference, the task of judging whether one sentence is true or false given that some other sentence is true, and argue that that task is distinctly effective as a means of developing and evaluating sentence understanding models in NLP. I’ll cover the recent datasets SNLI and MultiNLI. I’ll then turn to syntax, and present our work on the Corpus of Linguistic Acceptability (CoLA), an NLP challenge dataset based on the classic linguistic task of acceptability judgment.