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2019 EMNLP EMNLP 2019

BERT for Coreference Resolution: Baselines and Analysis

AbstractWe apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

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