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2022 ACL ACL 2022

Graph Refinement for Coreference Resolution

Abstract

AbstractThe state-of-the-art models for coreference resolution are based on independent mention pair-wise decisions. We propose a modelling approach that learns coreference at the document-level and takes global decisions. For this purpose, we model coreference links in a graph structure where the nodes are tokens in the text, and the edges represent the relationship between them. Our model predicts the graph in a non-autoregressive manner, then iteratively refines it based on previous predictions, allowing global dependencies between decisions. The experimental results show improvements over various baselines, reinforcing the hypothesis that document-level information improves conference resolution.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — mention pair-wise
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Lesly Miculicich , James Henderson

Topics

Machine Learning > Core Methods > Representation Learning Deep Learning > Architectures > Graph Neural Networks Natural Language Processing > Understanding > Coreference Resolution Deep Learning > Learning Types > Representation Learning

Keywords

coreference resolution document-level modeling non-autoregressive generation graph neural network graph refinement document-level understanding mention pair-wise
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