Sequential Cross Document Coreference Resolution
Relating entities and events in text is a key component of natural language understanding. Cross document coreference resolution, in particular, is important for the growing interest in multi document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross document settings and achieves competitive results for both entity and event coreference while provides strong evidence of the efficacy of both sequential models and higher order inference in cross document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
ArXiv