Abstract
Multiple-choice reading comprehension is a challenging task requiring a machine to select the correct answer from a candidate answers set. In this paper, we propose a model following a matching-integration-verification-prediction framework, which explicitly employs a verification module inspired by the human being and generates judgment of each option simultaneously according to the evidence information and the verified information. The verification module, which is responsible for recheck information from matching, can selectively combine matched information from the passage and option instead of transmitting them equally to prediction. Experimental results demonstrate that our proposed model achieves significant improvement on several multiple-choice reading comprehension benchmark datasets.
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Notes
- 1.
We will release our code upon publication.
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Acknowledgments
We thank the reviewers for their insightful comments. We also thank Effyic Intelligent Technology (Beijing) for their computing resource support. This work was supported by in part by the National Key Research and Development Program of China under Grant No. 2016YFB0801003.
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Xing, L., Hu, Y., Xie, Y., Wang, C., Hu, Y. (2020). A Matching-Integration-Verification Model for Multiple-Choice Reading Comprehension. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_21
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