%0 Conference Proceedings %T Constrained Multi-Task Learning for Bridging Resolution %A Kobayashi, Hideo %A Hou, Yufang %A Ng, Vincent %Y Muresan, Smaranda %Y Nakov, Preslav %Y Villavicencio, Aline %S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F kobayashi-etal-2022-constrained %X 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) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora. %R 10.18653/v1/2022.acl-long.56 %U https://aclanthology.org/2022.acl-long.56 %U https://doi.org/10.18653/v1/2022.acl-long.56 %P 759-770