Retrospective Multi-granularity Fusion Network for Chinese Idiom Cloze-style Reading Comprehension

Abstract

Chinese idiom cloze-style reading comprehension task is of great significance for improving the machine’s ability to understand Chinese idioms, which is one of the essential application requirements in advanced artificial intelligence. Existing methods suffer from an insufficient deep semantic understanding of the text. To solve this problem, this paper proposes a novelRetrospective Multi-granularity Fusion Network (RMFNet)for Chinese idiom cloze-style reading comprehension. Our RMFNet is equipped with two novel modules to model deeper contextual information of passage and Chinese idioms, respectively. First, we propose a novelMulti-granularity Passage Fusion (MgPF)module, which enhances the passage representation by integrating different semantic perspectives. Second, we propose aRetrospective Reading (Re\(^2\))module that implements a back-and-forth reading mechanism to concentrate on critical Chinese idioms, thereby generating an ultimate memory for the whole text. Notably, the intuition of the MgPF module and the Re\(^2\)module is based on human reading strategies in the real world. The strategies in these modules are similar to how humans perceive the text. Extensive experiments are conducted on Chinese benchmark datasets to evaluate the effectiveness and superiority of the proposed method. Our RMFNet achieves state-of-the-art performance and in-depth analysis verifies its capability for understanding the deep semantics of the text.