Contrastive Learning between Classical and Modern Chinese for Classical Chinese Machine Reading Comprehension

Abstract

By leveraging self-supervised tasks, pre-trained language model (PLM) has made significant progress in the field of machine reading comprehension (MRC) . However, in classical Chinese MRC (CCMRC) , the passage is typically in classical style, but the question and options are given in modern style. Existing pre-trained methods seldom model the relationship between classical and modern styles, resulting in overall misunderstanding of the passage. In this paper, we propose a contrastive learning method between classical and modern Chinese in order to reach a deep understanding of the two different styles. In particular, a novel pre-training task and an enhanced co-matching network have been defined: (1) The synonym discrimination (SD) task is used to identify whether modern meaning corresponds to classical Chinese. (2) The enhanced dual co-matching (EDCM) network is employed for a more interactive understanding of the classical passage and the modern options. The experimental results show that our proposed method improves language understanding ability and outperforms existing PLMs on the Haihua, CCLUE, and ChID datasets.