Probing the Linguistic Strengths and Limitations of Unsupervised Grammar Induction
By Yonatan Bisk, Julia Hockenmaier. 2015
14 citations recorded by the source corpus.
Work in grammar induction should help shed light on the amount of syntactic structure that is discoverable from raw word or tag sequences. But since most current grammar induction algorithms produce unlabeled dependencies, it is difficult to analyze what types of constructions these algorithms can or cannot capture, and, therefore, to identify where additional supervision may be necessary. This paper provides an in-depth analysis of the errors made by unsupervised CCG parsers by evaluating them against the labeled dependencies in CCGbank, hinting at new research directions necessary for progress in grammar induction.
Subfields: Natural Language Processing
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In Natural Language Processing
- Mohit Bansal
- Yejin Choi
- Graham Neubig
- Mirella Lapata
- Richard Socher
- Jacob Andreas
- Luke S. Zettlemoyer
- Christopher D. Manning
Citations in the corpus
- Experience Grounds Language (2020, background): “This may prove necessary to learn headedness (e.g. choosing the main verb vs the more common auxiliary as the root of a sentence) (Bisk and Hockenmaier, 2015).”