Challenges to Open-Domain Constituency Parsing

Sen Yang, Leyang Cui, Ruoxi Ning, Di Wu, Yue Zhang


Abstract
Neural constituency parsers have reached practical performance on news-domain benchmarks. However, their generalization ability to other domains remains weak. Existing findings on cross-domain constituency parsing are only made on a limited number of domains. Tracking this, we manually annotate a high-quality constituency treebank containing five domains. We analyze challenges to open-domain constituency parsing using a set of linguistic features on various strong constituency parsers. Primarily, we find that 1) BERT significantly increases parsers’ cross-domain performance by reducing their sensitivity on the domain-variant features.2) Compared with single metrics such as unigram distribution and OOV rate, challenges to open-domain constituency parsing arise from complex features, including cross-domain lexical and constituent structure variations.
Anthology ID:
2022.findings-acl.11
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
112–127
Language:
URL:
https://aclanthology.org/2022.findings-acl.11
DOI:
10.18653/v1/2022.findings-acl.11
Bibkey:
Cite (ACL):
Sen Yang, Leyang Cui, Ruoxi Ning, Di Wu, and Yue Zhang. 2022. Challenges to Open-Domain Constituency Parsing. In Findings of the Association for Computational Linguistics: ACL 2022, pages 112–127, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Challenges to Open-Domain Constituency Parsing (Yang et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2022.findings-acl.11.pdf
Code
 ringos/multi-domain-parsing-analysis +  additional community code
Data
Penn Treebank