A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

• URL: http://arxiv.org/abs/2303.18162v1
• Date: Fri, 31 Mar 2023 15:54:54 GMT
• Title: A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education
• Authors: Son T. Luu, Khoi Trong Hoang, Tuong Quang Pham, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: ViMMRC 2.0 is an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models.
• Score: 2.5199066832791535
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language.

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