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2022 ACL ACL 2022

Challenges to Open-Domain Constituency Parsing

Abstract

AbstractNeural 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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Application Areas > Domain Generalization Natural Language Processing > Understanding > Parsing Machine Learning > Learning Types > Transfer Learning Machine Learning > Learning Types > Deep Learning Machine Learning > Learning Types > Domain Generalization Deep Learning > Learning Types > Transfer Learning Natural Language Processing > Applications > Parsing

Keywords

domain adaptation constituency parsing cross-domain generalization syntactic structure neural parser cross-domain parsing lexical variation neural network
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