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- W4306705232 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." @default.
- W4306705232 created "2022-10-19" @default.
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- W4306705232 date "2022-10-14" @default.
- W4306705232 modified "2023-09-24" @default.
- W4306705232 title "Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution" @default.
- W4306705232 doi "https://doi.org/10.48550/arxiv.2210.07602" @default.
- W4306705232 hasPublicationYear "2022" @default.
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