Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution

Although, recent advances in neural network models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer those models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches for domain adaptation involve continued training on coreference annotations in the target domain, but obtaining those annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. Through timed annotation experiments, we also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for effectively adapting coreference models that requires only mention annotations in the target domain. We use an auxiliary mention detection objective trained with mention examples in the target domain resulting in higher mention precision. We demonstrate that our approach facilitates sample- and time-efficient transfer to new annotation schemes and lexicons in extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical case notes), and a dataset of child welfare case notes. We show that annotating mentions results in 7-14% improvement in average F1 over annotating coreference over an equivalent amount of time.