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Adversarial Discriminative Denoising for Distant Supervision Relation Extraction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11448)

Abstract

Distant supervision has been widely used to generate labeled data automatically for relation extraction by aligning knowledge base with text. However, it introduces much noise, which can severely impact the performance of relation extraction. Recent studies have attempted to remove the noise explicitly from the generated data but they suffer from (1) the lack of an effective way of introducing explicit supervision to the denoising process and (2) the difficulty of optimization caused by the sampling action in denoising result evaluation. To solve these issues, we propose an adversarial discriminative denoising framework, which provides an effective way of introducing human supervision and exploiting it along with the potentially useful information underlying the noisy data in a unified framework. Besides, we employ a continuous approximation of sampling action to guarantee the holistic denoising framework to be differentiable. Experimental results show that very little human supervision is sufficient for our approach to outperform the state-of-the-art methods significantly.

Keywords

Distant supervision Relation extraction Noise reduction Adversarial discriminative model 

Notes

Acknowledgement

This work was supported by National Key R&D Program of China (2018YFC0830200) and National Natural Science Foundation of China Key Project (U1736204).

References

  1. 1.
    Feng, J., Huang, M., Zhao, L., Yang, Y., Zhu, X.: Reinforcement learning for relation classification from noisy data. In: Proceedings of AAAI, pp. 5779–5786 (2018)Google Scholar
  2. 2.
    Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of ACL, pp. 2124–2133 (2016)Google Scholar
  3. 3.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of ACL, pp. 1003–1011 (2009)Google Scholar
  4. 4.
    Qin, P., Xu, W., Wang, W.Y.: DSGAN: generative adversarial training for distant supervision relation extraction. In: Proceedings of ACL, pp. 496–505 (2018)Google Scholar
  5. 5.
    Qin, P., Xu, W., Wang, W.Y.: Robust distant supervision relation extraction via deep reinforcement learning. In: Proceedings of ACL, pp. 2137–2147 (2018)Google Scholar
  6. 6.
    Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 148–163. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15939-8_10CrossRefGoogle Scholar
  7. 7.
    Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of EMNLP, pp. 1753–1762 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Computer Network and Information Integration, Ministry of EducationSoutheast UniversityNanjingChina
  3. 3.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore

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