Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. MRC systems must not only answer question when necessary but also distinguish when no answer is available according to the given passage and then tactfully abstain from answering. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still most benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yield an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than the strong ALBERT baseline. A series of analysis is also conducted to interpret the effectiveness of the proposed reader.

本文旨在探索更好的验证模块设计以解决包含难以回答的问题的机器阅读理解任务。我们提出了一个后验阅读器（Retro-Reader），通过将两个阅读和验证策略整合到一起进行机器阅读和理解，旨在模仿人类解决阅读理解问题的过程，并在两个基准 MRC 数据集 SQuAD2.0 和 NewsQA 上取得了最新的最优结果。经过显著性测试，我们的模型明显优于强大的 ELECTRA 和 ALBERT 基线，同时还进行了一系列分析来解释所提出的阅读器的有效性。

机器阅读理解的回顾式阅读器