Chinese Implicit Discourse Relation Recognition Based on Data Augmentation

Abstract

Due to the lack of connectives,implicit discourse relation recognition is a challenging task,especially in Chinese.This paper proposes a method for Chinese implicit discourse relation recognition,which expands the training data by combining active learning and multi-task learning method.This method aims to reduce the noise as much as possible when it expands the training data set.Firstly,the active learning is used to select some explicit data through the classification uncertainty based on BERT,and then the connectives in the explicit data are removed and regarded as pseudo-implicit training data.Finally,a multi task learning method is used to boost implicit discourse relation recognition by using the pseudo-implicit training data.Experimental results on Chinese discourse treebank (CDTB) show that our method improves the macro-average F1 and micro-average F1 scores,compared with the baselines.

Keywords

• discourse parsing|implicit discourse relation recognition|active learning|multi-task learning