mlsanityData to 7 Oct 2026

Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering

arXiv 1805.04893 ↗First posted 13 May 2018cs.CLcited by 16

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Abstract (arXiv)

Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity. The state-of-the-art end-to-end neural coreference model considers all text spans in a document as potential mentions and learns to link an antecedent for each possible mention. In this paper, we propose to improve the end-to-end coreference resolution system by (1) using a biaffine attention model to get antecedent scores for each possible mention, and (2) jointly optimizing the mention detection accuracy and the mention clustering log-likelihood given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 Shared Task English test set.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2018.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 1; 2024: 4; 2025: 0; 2026: 1.

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