Learning Syntax from Naturally-Occurring Bracketings

Tianze Shi, Ozan İrsoy, Igor Malioutov, Lillian Lee


Abstract
Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsupervised constituency parsing. But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning. Experiments demonstrate that our distantly-supervised models trained on naturally-occurring bracketing data are more accurate in inducing syntactic structures than competing unsupervised systems. On the English WSJ corpus, our models achieve an unlabeled F1 score of 68.9 for constituency parsing.
Anthology ID:
2021.naacl-main.234
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2941–2949
Language:
URL:
https://aclanthology.org/2021.naacl-main.234
DOI:
10.18653/v1/2021.naacl-main.234
Bibkey:
Cite (ACL):
Tianze Shi, Ozan İrsoy, Igor Malioutov, and Lillian Lee. 2021. Learning Syntax from Naturally-Occurring Bracketings. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2941–2949, Online. Association for Computational Linguistics.
Cite (Informal):
Learning Syntax from Naturally-Occurring Bracketings (Shi et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.234.pdf
Video:
 https://aclanthology.org/2021.naacl-main.234.mp4
Code
 tzshi/nob-naacl21
Data
QA-SRL