@inproceedings{aralikatte-etal-2019-rewarding,
title = "Rewarding Coreference Resolvers for Being Consistent with World Knowledge",
author = "Aralikatte, Rahul and
Lent, Heather and
Gonzalez, Ana Valeria and
Hershcovich, Daniel and
Qiu, Chen and
Sandholm, Anders and
Ringaard, Michael and
S{\o}gaard, Anders",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1118",
doi = "10.18653/v1/D19-1118",
pages = "1229--1235",
abstract = "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 performance, improving over the state of the art, using multi-task reinforcement learning.",
}
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%0 Conference Proceedings
%T Rewarding Coreference Resolvers for Being Consistent with World Knowledge
%A Aralikatte, Rahul
%A Lent, Heather
%A Gonzalez, Ana Valeria
%A Hershcovich, Daniel
%A Qiu, Chen
%A Sandholm, Anders
%A Ringaard, Michael
%A Søgaard, Anders
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F aralikatte-etal-2019-rewarding
%X 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 performance, improving over the state of the art, using multi-task reinforcement learning.
%R 10.18653/v1/D19-1118
%U https://aclanthology.org/D19-1118
%U https://doi.org/10.18653/v1/D19-1118
%P 1229-1235
Markdown (Informal)
[Rewarding Coreference Resolvers for Being Consistent with World Knowledge](https://aclanthology.org/D19-1118) (Aralikatte et al., EMNLP-IJCNLP 2019)
ACL
- Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, and Anders Søgaard. 2019. Rewarding Coreference Resolvers for Being Consistent with World Knowledge. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1229–1235, Hong Kong, China. Association for Computational Linguistics.