Decomposed Local Models for Coordinate Structure Parsing

Hiroki Teranishi, Hiroyuki Shindo, Yuji Matsumoto


Abstract
We propose a simple and accurate model for coordination boundary identification. Our model decomposes the task into three sub-tasks during training; finding a coordinator, identifying inside boundaries of a pair of conjuncts, and selecting outside boundaries of it. For inference, we make use of probabilities of coordinators and conjuncts in the CKY parsing to find the optimal combination of coordinate structures. Experimental results demonstrate that our model achieves state-of-the-art results, ensuring that the global structure of coordinations is consistent.
Anthology ID:
N19-1343
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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3394–3403
Language:
URL:
https://aclanthology.org/N19-1343
DOI:
10.18653/v1/N19-1343
Bibkey:
Cite (ACL):
Hiroki Teranishi, Hiroyuki Shindo, and Yuji Matsumoto. 2019. Decomposed Local Models for Coordinate Structure Parsing. 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 3394–3403, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Decomposed Local Models for Coordinate Structure Parsing (Teranishi et al., NAACL 2019)
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PDF:
https://aclanthology.org/N19-1343.pdf