Graph Refinement for Coreference Resolution
This addresses coreference resolution for NLP applications, offering an incremental advance by incorporating global dependencies.
The paper tackles coreference resolution by proposing a document-level modeling approach using graph structures and iterative refinement, achieving improvements over various baselines.
The 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.