论文标题
仅提及注释就可以为核心解决方案提供有效的域适应性
Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution
论文作者
论文摘要
尽管最近用于核心分辨率的神经模型导致了基准数据集的实质性改进，但将这些模型转移到包含不播放外跨度跨度的新目标域，并且需要不同的注释方案仍然具有挑战性。典型的方法涉及对带注释的目标域数据的持续培训，但获得注释是昂贵且耗时的。我们表明，单独的注释提及的速度几乎是注释完整的核心链的两倍。因此，我们提出了一种有效调整核心模型的方法，其中包括高精度提及的检测目标，并且需要注释目标域中的提及。对三个英语核心数据集进行了广泛的评估：Conll-2012（新闻/对话），I2B2/VA（医疗笔记）以及以前未研究的儿童福利笔记，表明我们的方法有助于注释有效的转移，并导致平均F1的7-14％改善，而无需增加注释时间。
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.
