Adapting Coreference Resolution Models through Active Learning
Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, Jordan Boyd‐Graber
Abstract
Neural coreference resolution models trained on one dataset may not transfer to new, lowresource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.