Investigating Non-local Features for Neural Constituency Parsing

Leyang Cui, Sen Yang, Yue Zhang


Abstract
Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.
Anthology ID:
2022.acl-long.146
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2065–2075
Language:
URL:
https://aclanthology.org/2022.acl-long.146
DOI:
10.18653/v1/2022.acl-long.146
Bibkey:
Cite (ACL):
Leyang Cui, Sen Yang, and Yue Zhang. 2022. Investigating Non-local Features for Neural Constituency Parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2065–2075, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Investigating Non-local Features for Neural Constituency Parsing (Cui et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.146.pdf
Software:
 2022.acl-long.146.software.zip
Code
 ringos/nfc-parser