Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
Abstract
Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.- Anthology ID:
- D19-1118
- Original:
- D19-1118v1
- Version 2:
- D19-1118v2
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1229–1235
- Language:
- URL:
- https://aclanthology.org/D19-1118
- DOI:
- 10.18653/v1/D19-1118
- Cite (ACL):
- Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, and Anders Søgaard. 2019. Rewarding Coreference Resolvers for Being Consistent with World Knowledge. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1229–1235, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Rewarding Coreference Resolvers for Being Consistent with World Knowledge (Aralikatte et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/D19-1118.pdf
- Code
- rahular/coref-rl
- Data
- DBpedia, WikiCoref