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In spite of much recent research in the area, it is still unclear whethersubject-area question-answering data is useful for machine readingcomprehension (MRC) tasks. In this paper, we investigate this question. Wecollect a large-scale multi-subject multiple-choice question-answering dataset,ExamQA, and use incomplete and noisy snippets returned by a web search engineas the relevant context for each question-answering instance to convert it intoa weakly-labeled MRC instance. We then propose a self-teaching paradigm tobetter use the generated weakly-labeled MRC instances to improve a target MRCtask. Experimental results show that we can obtain +5.1% in accuracy on amultiple-choice MRC dataset, C^3, and +3.8% in exact match on an extractive MRCdataset, CMRC 2018 over state-of-the-art MRC baselines, demonstrating theeffectiveness of our framework and the usefulness of large-scale subject-areaquestion-answering data for different types of machine reading comprehensiontasks.

Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data