Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

• URL: http://arxiv.org/abs/2104.10215v1
• Date: Tue, 20 Apr 2021 19:14:57 GMT
• Title: Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
• Authors: Sopan Khosla, James Fiacco, Carolyn Rose
• Abstract summary: In this work, we leverage automatically constructed discourse parse trees within a neural approach. We demonstrate a significant improvement on two benchmark entity coreference-resolution datasets.
• Score: 3.7277082975620797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.

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