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

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Machine Reading Comprehension (MRC) is a challenging NLP task focused on enabling machines to understand text and answer questions.
• Existing Chinese MRC methods often suffer from pipeline integration issues, leading to re-encoding problems and inconsistent data distribution.

Purpose of the Study:

• To propose an efficient and accurate method for Chinese Machine Reading Comprehension.
• To address the limitations of traditional pipeline-based MRC systems.

Main Methods:

• Developed an Improved Extraction-based Reading Comprehension with Answer Re-ranking (IERC-AR) model.
• Utilized RoBERTa-WWM for precise word representations to handle polysemy and capture Chinese word-level features.
• Implemented a self-attention mechanism in the re-ranking module to refine candidate answers.
• Designed an end-to-end model architecture to integrate extraction and re-ranking modules seamlessly.

Main Results:

• The IERC-AR model demonstrated superior performance on the Les MMRC dataset.
• Achieved state-of-the-art results compared to existing MRC approaches.
• The improved word representations and re-ranking mechanism significantly boosted prediction accuracy.

Conclusions:

• The proposed IERC-AR model effectively enhances Chinese Machine Reading Comprehension.
• The end-to-end architecture resolves issues associated with traditional pipeline systems.
• IERC-AR represents a significant advancement in the field of NLP for Chinese text understanding.