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        <title>Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.</abstract>
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%0 Conference Proceedings
%T Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
%A Qin, Pengda
%A Xu, Weiran
%A Wang, William Yang
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F qin-etal-2018-robust
%X Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.
%R 10.18653/v1/P18-1199
%U https://aclanthology.org/P18-1199
%U https://doi.org/10.18653/v1/P18-1199
%P 2137-2147

Markdown (Informal)

[Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](https://aclanthology.org/P18-1199) (Qin et al., ACL 2018)

• Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning (Qin et al., ACL 2018)

ACL

• Pengda Qin, Weiran Xu, and William Yang Wang. 2018. Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2137–2147, Melbourne, Australia. Association for Computational Linguistics.