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    <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.</abstract>
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%0 Conference Proceedings
%T Challenges to Open-Domain Constituency Parsing
%A Yang, Sen
%A Cui, Leyang
%A Ning, Ruoxi
%A Wu, Di
%A Zhang, Yue
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F yang-etal-2022-challenges
%X 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.
%R 10.18653/v1/2022.findings-acl.11
%U https://aclanthology.org/2022.findings-acl.11
%U https://doi.org/10.18653/v1/2022.findings-acl.11
%P 112-127

Markdown (Informal)

[Challenges to Open-Domain Constituency Parsing](https://aclanthology.org/2022.findings-acl.11) (Yang et al., Findings 2022)

• Challenges to Open-Domain Constituency Parsing (Yang et al., Findings 2022)

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.