Multi-passage extraction-based machine reading comprehension based on verification sorting

Abstract

For traditional single-passage machine reading comprehension, the text data of a single passage does not well reflect the complexity of practical application scenarios. Many researchers have shifted their research goals to study multi-passage machine reading comprehension. To solve the problem of multi-passage machine reading comprehension, this paper proposes a unified multi-module end-to-end reading comprehension model for passage retrieval, answer extraction, and multi-answer verification ranking using a pre-trained model. In this paper, the passage retrieval module selects the passage fragments with the highest probability of survival, and the answer extraction component extracts the possible candidate answers in each passage. The multi-answer verification ranking component uses an attention mechanism to fuse multiple candidate answer feature representations, obtains the score of each candidate answer and selects the candidate answer with the highest score as the final answer. Finally, through experimental validation, the proposed model achieves scores of 49.59 and 46.28 on the evaluation metrics BLUE-4 and ROUGH-L on the DuReader dataset, and scores of 44.78 and 46.45 on the metrics BLUE-1 and ROUGH-L on the MS-MARCO dataset, respectively.