Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
William Held, Dan Iter, Dan Jurafsky · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full n 2 pairwise comparisons.Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications.As a result cross-document coreference algorithms are rarely applied to downstream tasks.We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader's discourse focus.We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features.Our approach 1 achieves state-of-the-art results for both events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-Document Coreference corpora.Furthermore, training on multiple corpora improves average performance across all datasets by 17.2 F1 points, leading to a robust coreference resolution model for use in downstream tasks where link distribution is unknown.