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Unresolved coreference is a bottleneck for relation extraction, andhigh-quality coreference resolvers may produce an output that makes it a loteasier to extract knowledge triples. We show how to improve coreferenceresolvers by forwarding their input to a relation extraction system and rewardthe resolvers for producing triples that are found in knowledge bases. Sincerelation extraction systems can rely on different forms of supervision and bebiased in different ways, we obtain the best performance, improving over thestate of the art, using multi-task reinforcement learning.

Rewarding Coreference Resolvers for Being Consistent with World Knowledge