Neural Constituency Parsing of Speech Transcripts

Paria Jamshid Lou, Yufei Wang, Mark Johnson

Abstract
This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation and the presence of speech disfluencies (including filled pauses, repetitions, corrections, etc.). Disfluencies are especially problematic for conventional syntactic parsers, which typically fail to find any EDITED disfluency nodes at all. This motivated the development of special disfluency detection systems, and special mechanisms added to parsers specifically to handle disfluencies. However, we show here that neural parsers can find EDITED disfluency nodes, and the best neural parsers find them with an accuracy surpassing that of specialized disfluency detection systems, thus making these specialized mechanisms unnecessary. This paper also investigates a modified loss function that puts more weight on EDITED nodes. It also describes tree-transformations that simplify the disfluency detection task by providing alternative encodings of disfluencies and syntactic information.
Anthology ID:
N19-1282
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2756–2765
Language:
URL:
https://aclanthology.org/N19-1282
DOI:
10.18653/v1/N19-1282
Bibkey:
Cite (ACL):
Paria Jamshid Lou, Yufei Wang, and Mark Johnson. 2019. Neural Constituency Parsing of Speech Transcripts. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2756–2765, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Neural Constituency Parsing of Speech Transcripts (Jamshid Lou et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/improve-issue-templates/N19-1282.pdf
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