Integrate Candidate Answer Extraction with Re-Ranking for Chinese Machine Reading Comprehension

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 3; p. 322
• Main Authors: Zeng, Junjie, Sun, Xiaoya, Zhang, Qi, Li, Xinmeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 08.03.2021; MDPI AG
• Subjects: answer re-ranking; extraction-based machine reading comprehension; pre-training language model; self-attention; self-attention; answer re-ranking; pre-training language model; extraction-based machine reading comprehension
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23030322

Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing. To solve the Chinese MRC task efficiently, this paper proposes an Improved Extraction-based Reading Comprehension method with Answer Re-ranking (IERC-AR), consisting of a candidate answer extraction module and a re-ranking module. The candidate answer extraction module uses an improved pre-training language model, RoBERTa-WWM, to generate precise word representations, which can solve the problem of polysemy and is good for capturing Chinese word-level features. The re-ranking module re-evaluates candidate answers based on a self-attention mechanism, which can improve the accuracy of predicting answers. Traditional machine-reading methods generally integrate different modules into a pipeline system, which leads to re-encoding problems and inconsistent data distribution between the training and testing phases; therefore, this paper proposes an end-to-end model architecture for IERC-AR to reasonably integrate the candidate answer extraction and re-ranking modules. The experimental results on the Les MMRC dataset show that IERC-AR outperforms state-of-the-art MRC approaches.