Incorporating Context and External Knowledge for Pronoun Coreference Resolution

Hongming Zhang, Yan Song, Yangqiu Song


Abstract
Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism is designed to ensure the model leveraging the appropriate source of external knowledge based on different context. Experimental results demonstrate the validity and effectiveness of our model, where it outperforms state-of-the-art models by a large margin.
Anthology ID:
N19-1093
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
872–881
Language:
URL:
https://aclanthology.org/N19-1093
DOI:
10.18653/v1/N19-1093
Bibkey:
Cite (ACL):
Hongming Zhang, Yan Song, and Yangqiu Song. 2019. Incorporating Context and External Knowledge for Pronoun Coreference Resolution. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 872–881, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Incorporating Context and External Knowledge for Pronoun Coreference Resolution (Zhang et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1093.pdf
Code
 HKUST-KnowComp/Pronoun-Coref