Abstract
Distant supervision is widely used in relation classification in order to create large-scale training data by aligning a knowledge base with an unlabeled corpus. However, it also introduces amounts of noisy labels where a contextual sentence actually does not express the labeled relation. In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification. ARNOR assumes that a trustable relation label should be explained by the neural attention model. Specifically, our ARNOR framework iteratively learns an interpretable model and utilizes it to select trustable instances. We first introduce attention regularization to force the model to pay attention to the patterns which explain the relation labels, so as to make the model more interpretable. Then, if the learned model can clearly locate the relation patterns of a candidate instance in the training set, we will select it as a trustable instance for further training step. According to the experiments on NYT data, our ARNOR framework achieves significant improvements over state-of-the-art methods in both relation classification performance and noise reduction effect.- Anthology ID:
- P19-1135
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1399–1408
- Language:
- URL:
- https://aclanthology.org/P19-1135
- DOI:
- 10.18653/v1/P19-1135
- Cite (ACL):
- Wei Jia, Dai Dai, Xinyan Xiao, and Hua Wu. 2019. ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1399–1408, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification (Jia et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P19-1135.pdf