Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues

TL;DR

This paper introduces an adapted end-to-end neural coreference resolution system tailored for dialogues, enhancing anaphora resolution by supporting singletons, encoding speaker-turn information, and transferring knowledge, leading to significant performance improvements.

Contribution

The paper presents a simple yet effective adaptation of existing models for dialogue-specific coreference resolution, achieving state-of-the-art results.

Findings

Up to 27 F1 improvement over baseline

Ranked 1st in CRAC 2021 shared task

Achieved best results on four datasets

Abstract

We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues. Three aspects are specifically addressed in our approach, including the support of singletons, encoding speakers and turns throughout dialogue interactions, and knowledge transfer utilizing existing resources. Despite the simplicity of our adaptation strategies, they are shown to bring significant impact to the final performance, with up to 27 F1 improvement over the baseline. Our final system ranks the 1st place on the leaderboard of the anaphora resolution track in the CRAC 2021 shared task, and achieves the best evaluation results on all four datasets.