Adversarial Training for Relation Classification with Attention based Gate Mechanism | |
Pengfei Cao1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2 | |
2018-07-20 | |
会议日期 | 14-17, August, 2018 |
会议地点 | Tianjin, China |
英文摘要 | In recent years, deep neural networks have achieved significant success in relation classification and many other natural language processing tasks. However, existing neural networks for relation classification heavily rely on the quality of labelled data and tend to be overconfident about the noise in input signals. They may be limited in robustness and generalization. In this paper, we apply adversarial training to the relation classification by adding perturbations to the input vectors in bidirectional long short-term memory neural networks rather than to the original input itself. Besides, we propose an attention based gate module, which can not only discern the important information when learning the sentence representations but also adaptively concatenate sentence level and lexical level features. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms other state-of-the-art models. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52186] ![]() |
专题 | 模式识别国家重点实验室_自然语言处理 |
作者单位 | 1.Institute of Automation Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Pengfei Cao,Yubo Chen,Kang Liu,et al. Adversarial Training for Relation Classification with Attention based Gate Mechanism[C]. 见:. Tianjin, China. 14-17, August, 2018. |
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