Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract) release_tusk5cjd45hq5pam2akz354gpm

by Yuxiang Xie, Hua Xu, Congcong Yang, Kai Gao

Published in PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE by Association for the Advancement of Artificial Intelligence (AAAI).

2020   Volume 34, Issue 10, p13967-13968

Abstract

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.
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Date   2020-04-03
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