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.- Anthology ID:
- D17-1187
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1778–1783
- Language:
- URL:
- https://aclanthology.org/D17-1187
- DOI:
- 10.18653/v1/D17-1187
- Cite (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.
- Cite (Informal):
- Adversarial Training for Relation Extraction (Wu et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D17-1187.pdf