Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Herschcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
<span title="">2019</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3ideoxy4fghvbsstiknuweth4" style="color: black;">Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</a> </i> &nbsp;
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. 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
more &raquo; ... ormance, improving over the state of the art, using multi-task reinforcement learning. tic Web Conference, ISWC'07/ASWC'07, pages 722-735, Berlin, Heidelberg. Springer-Verlag. . 2014. Typed tensor decomposition of knowledge bases for relation extraction. In EMNLP. Kevin Clark and Christopher D Manning. 2016. Deep reinforcement learning for mention-ranking coreference models. In
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