Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

• URL: http://arxiv.org/abs/2110.05362v1
• Date: Mon, 11 Oct 2021 15:41:47 GMT
• Title: Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
• Authors: William Held, Dan Iter, Dan Jurafsky
• Abstract summary: Event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full $n2$ pairwise comparisons. Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference. We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader's discourse focus. Our approach 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.
• Score: 22.497877069528087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

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