Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

• URL: http://arxiv.org/abs/2309.11582v1
• Date: Wed, 20 Sep 2023 18:44:24 GMT
• Title: Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization
• Authors: Yilun Zhu, Siyao Peng, Sameer Pradhan, Amir Zeldes
• Abstract summary: This paper presents a coreference model that learns singletons as well as features such as entity type and information status. This approach achieves new state-of-the-art scores on the OntoGUM benchmark.
• Score: 12.084539012992412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as well as other entity information. This paper presents a coreference model that learns singletons as well as features such as entity type and information status via a multi-task learning-based approach. This approach achieves new state-of-the-art scores on the OntoGUM benchmark (+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3 points on average), likely due to greater generalizability for mention detection and utilization of more data from singletons when compared to only coreferent mention pair matching.

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