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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.acl-long.146.pdf
- Code
- ringos/nfc-parser
- Data
- Penn Treebank