Abstract
Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.- Anthology ID:
- 2021.emnlp-main.382
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4659–4671
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.382
- DOI:
- 10.18653/v1/2021.emnlp-main.382
- Cite (ACL):
- Emily Allaway, Shuai Wang, and Miguel Ballesteros. 2021. Sequential Cross-Document Coreference Resolution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4659–4671, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Sequential Cross-Document Coreference Resolution (Allaway et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.emnlp-main.382.pdf
- Data
- ECB+