Abstract
Event coreference resolution (ECR) aims to cluster event mentions that refer to the same real-world events. Deep learning methods have achieved SOTA results on the ECR task. However, due to the encoding length limitation, previous methods either adopt classical pairwise models based on sentence-level context or split each document into multiple chunks and encode them separately. They failed to capture the interactions and contextual cues among those long-distance event mentions. Besides, high-level information, such as event topics, is rarely considered to enhance representation learning for ECR. To address the above two issues, we first apply a Longformer-based encoder to obtain the document-level embeddings and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context. In addition, we propose an event topic generator to infer the latent topic-level representations. Finally, using the above event embeddings, we employ a multiple tensor matching method to capture their interactions at the document, sentence, and topic levels. Experimental results on the KBP 2017 dataset show that our model outperforms the SOTA baselines.- Anthology ID:
- 2022.emnlp-main.454
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6765–6775
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.454
- DOI:
- 10.18653/v1/2022.emnlp-main.454
- Cite (ACL):
- Sheng Xu, Peifeng Li, and Qiaoming Zhu. 2022. Improving Event Coreference Resolution Using Document-level and Topic-level Information. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6765–6775, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Improving Event Coreference Resolution Using Document-level and Topic-level Information (Xu et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.emnlp-main.454.pdf