@inproceedings{lu-etal-2023-anaphor,
title = "Anaphor Assisted Document-Level Relation Extraction",
author = "Lu, Chonggang and
Zhang, Richong and
Sun, Kai and
Kim, Jaein and
Zhang, Cunwang and
Mao, Yongyi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.955",
doi = "10.18653/v1/2023.emnlp-main.955",
pages = "15453--15464",
abstract = "Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods. On the other hand, these methods achieve cross-sentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes. Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.",
}
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<abstract>Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods. On the other hand, these methods achieve cross-sentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes. Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Anaphor Assisted Document-Level Relation Extraction
%A Lu, Chonggang
%A Zhang, Richong
%A Sun, Kai
%A Kim, Jaein
%A Zhang, Cunwang
%A Mao, Yongyi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lu-etal-2023-anaphor
%X Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods. On the other hand, these methods achieve cross-sentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes. Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.
%R 10.18653/v1/2023.emnlp-main.955
%U https://aclanthology.org/2023.emnlp-main.955
%U https://doi.org/10.18653/v1/2023.emnlp-main.955
%P 15453-15464
Markdown (Informal)
[Anaphor Assisted Document-Level Relation Extraction](https://aclanthology.org/2023.emnlp-main.955) (Lu et al., EMNLP 2023)
ACL
- Chonggang Lu, Richong Zhang, Kai Sun, Jaein Kim, Cunwang Zhang, and Yongyi Mao. 2023. Anaphor Assisted Document-Level Relation Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15453–15464, Singapore. Association for Computational Linguistics.