Paired Representation Learning for Event and Entity Coreference
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Co-reference of Events and of Entities are commonly formulated as binary classification problems, given a pair of events or entities as input. Earlier work addressed the main challenge in these problems – the representation of each element in the input pair by: (i) modelling the representation of one element (event or entity) without considering the other element in the pair; (ii) encoding all attributes of one element (e.g., arguments of an event) into a single non-interpretable vector, thus losing the ability to compare cross-element attributes. In this work we propose paired representation learning (PairedRL) for coreference resolution. Given a pair of elements (Events or Entities) our model treats the pair's sentences as a single sequence so that each element in the pair learns its representation by encoding its own context as well the other element's context. In addition, when representing events, PairedRL is structured in that it represents the event's arguments to facilitate their individual contribution to the final prediction. As we show, in both (within-document cross-document) event and entity coreference benchmarks, our unified approach, PairedRL, outperforms prior state of the art systems with a large margin.
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