Constrained Multi-Task Learning for Bridging Resolution

Hideo Kobayashi, Yufang Hou and Vincent Ng.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 759-770, 2022.

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Abstract

We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrate prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.

BibTeX entry

@InProceedings{Kobayashi+etal:22a,
  author = {Hideo Kobayashi and Yufang Hou and Vincent Ng},
  title = {Constrained Multi-Task Learning for Bridging Resolution},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages = {759--770}, 
  year = 2022}