About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ACL 2022
Conference paper
Constrained Multi-Task Learning for Bridging Resolution
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) pretrain 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-basedresolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.