Abstract
Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.- Anthology ID:
- P19-1064
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 660–665
- Language:
- URL:
- https://aclanthology.org/P19-1064
- DOI:
- 10.18653/v1/P19-1064
- Cite (ACL):
- Hongliang Fei, Xu Li, Dingcheng Li, and Ping Li. 2019. End-to-end Deep Reinforcement Learning Based Coreference Resolution. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 660–665, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- End-to-end Deep Reinforcement Learning Based Coreference Resolution (Fei et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1064.pdf
- Data
- CoNLL-2012, OntoNotes 5.0