Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution

• URL: http://arxiv.org/abs/2210.07602v2
• Date: Tue, 30 May 2023 22:07:45 GMT
• Title: Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution
• Authors: Nupoor Gandhi, Anjalie Field, Emma Strubell
• Abstract summary: We show that annotating mentions alone is nearly twice as fast as annotating full coreference chains. Our approach facilitates annotation-efficient transfer and results in a 7-14% improvement in average F1 without increasing annotator time.
• Score: 8.08448832546021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, transferring these models to new target domains containing out-of-vocabulary spans and requiring differing annotation schemes remains challenging. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. We show that annotating mentions alone is nearly twice as fast as annotating full coreference chains. Accordingly, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires annotating only mentions in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and previously unstudied child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14% improvement in average F1 without increasing annotator time.

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