A long-standing challenge in coreference resolution has been the incorporation of entity-level information -features defined over clusters of mentions instead of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our system learns when combining clusters is desirable. We train the system with a learning-to-search algorithm that teaches it which local decisions (cluster merges) will lead to a high-scoring final coreference partition. The system substantially outperforms the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task dataset despite using few hand-engineered features.
REF ] employed reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics, and achieved the best known performance on Chinese portion of the CoNLL 2012 Shared Task.
Improving Coreference Resolution by Learning Entity-Level Distributed Representations
ArXiv
ArXiv
Computer Science