Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract) - Xie, Yuxiang; Xu, Hua; Yang, Congcong; Gao, Kai (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.