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

• URL: http://arxiv.org/abs/2102.01226v1
• Date: Mon, 1 Feb 2021 23:18:58 GMT
• Title: Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question Answering Data
• Authors: Dian Yu, Kai Sun, Dong Yu, Claire Cardie
• Abstract summary: It is unclear whether subject-area question-answering data is useful for machine reading comprehension tasks. We collect a large-scale multi-subject multiple-choice question-answering dataset, ExamQA. We use incomplete and noisy snippets returned by a web search engine as the relevant context for each question-answering instance to convert it into a weakly-labeled MRC instance.
• Score: 58.36305373100518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this paper, we investigate this question. We collect a large-scale multi-subject multiple-choice question-answering dataset, ExamQA, and use incomplete and noisy snippets returned by a web search engine as the relevant context for each question-answering instance to convert it into a weakly-labeled MRC instance. We then propose a self-teaching paradigm to better use the generated weakly-labeled MRC instances to improve a target MRC task. Experimental results show that we can obtain an improvement of 5.1% in accuracy on a multiple-choice MRC dataset, C^3, demonstrating the effectiveness of our framework and the usefulness of large-scale subject-area question-answering data for machine reading comprehension.

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