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

Source: [Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data](<https://www.sota2.com/research/paper/68fa69ae1e209131cea23573>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:06:39.643Z

Authors: Dian Yu, Kai Sun, Dong Yu, Claire Cardie

Published: 2021-02-01T23:18:58.000Z

## 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 +5.1% in accuracy on a multiple-choice MRC dataset, C^3, and +3.8% in exact match on an extractive MRC dataset, CMRC 2018 over state-of-the-art MRC baselines, demonstrating the effectiveness of our framework and the usefulness of large-scale subject-area question-answering data for different types of machine reading comprehension tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2102.01226>)

[PDF](<https://arxiv.org/pdf/2102.01226.pdf>)