An Idiom Reading Comprehension Model Based on Multi-Granularity Reasoning and Paraphrase Expansion

Idioms are a unique class of words in the Chinese language that can be challenging for Chinese machine reading comprehension due to their formal simplicity and the potential mismatch between their literal and figurative meanings. To address this issue, this paper adopted the "2 + 2" structure as the representation model for idiom structure feature extraction. According to the linguistic theory of idioms, to enhance the model's learning ability for idiom semantics, we propose a two-stage semantic expansion method that leverages semantic knowledge during the pre-training stage and extracts idiom interpretation information during the fine-tuning stage to improve the model's understanding of idioms. Moreover, with the consideration of the inferential interaction between global and local information during context fusion, which is neglected by all current works, we propose a method that utilizes a multi-headed attention mechanism to fully extract global and local information by selecting three attention patterns in the fine-tuning stage. This enables the fusion of semantic information extracted by the model at different granularities, leading to improved accuracy. The experimental results demonstrated that our proposed BERT-IDM model outperformed the baseline BERT model by achieving a 4.1% accuracy improvement.