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        <title>Adversarial Training for Relation Extraction</title>
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        <namePart type="given">David</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1187</identifier>
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%0 Conference Proceedings
%T Adversarial Training for Relation Extraction
%A Wu, Yi
%A Bamman, David
%A Russell, Stuart
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wu-etal-2017-adversarial
%X Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.
%R 10.18653/v1/D17-1187
%U https://aclanthology.org/D17-1187
%U https://doi.org/10.18653/v1/D17-1187
%P 1778-1783

Markdown (Informal)

[Adversarial Training for Relation Extraction](https://aclanthology.org/D17-1187) (Wu et al., EMNLP 2017)

• Adversarial Training for Relation Extraction (Wu et al., EMNLP 2017)

ACL

• Yi Wu, David Bamman, and Stuart Russell. 2017. Adversarial Training for Relation Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1778–1783, Copenhagen, Denmark. Association for Computational Linguistics.