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Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Rahul Aralikatte
Heather Lent
Ana Valeria Gonzalez
Daniel Hershcovich
Chen Qiu
Michael Ringgaard
Anders Søgaard
EMNLP-IJCNLP 2019 (2019)


Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. In this paper, we show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, including significant improvements over the state of the art, using multi-task reinforcement learning.