Sentence Extraction-Based Machine Reading Comprehension for Vietnamese

• URL: http://arxiv.org/abs/2105.09043v1
• Date: Wed, 19 May 2021 10:22:27 GMT
• Title: Sentence Extraction-Based Machine Reading Comprehension for Vietnamese
• Authors: Phong Nguyen-Thuan Do, Nhat Duy Nguyen, Tin Van Huynh, Kiet Van Nguyen, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: We introduce the UIT-ViWikiQA, the first dataset for evaluating sentence extraction-based machine reading comprehension in Vietnamese language. The dataset consists of comprises 23.074 question-answers based on 5.109 passages of 174 Vietnamese articles from Wikipedia. Our experiments show that the best machine model is XLM-R$_Large, which achieves an exact match (EM) score of 85.97% and an F1-score of 88.77% on our dataset.
• Score: 0.2446672595462589
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of Vietnamese language processing in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension tasks in Vietnamese with large sizes, such as UIT-ViQuAD and UIT-ViNewsQA. However, the datasets are not diverse in answer to serve the research. In this paper, we introduce the UIT-ViWikiQA, the first dataset for evaluating sentence extraction-based machine reading comprehension in the Vietnamese language. The UIT-ViWikiQA dataset is converted from the UIT-ViQuAD dataset, consisting of comprises 23.074 question-answers based on 5.109 passages of 174 Vietnamese articles from Wikipedia. We propose a conversion algorithm to create the dataset for sentence extraction-based machine reading comprehension and three types of approaches on the sentence extraction-based machine reading comprehension for Vietnamese. Our experiments show that the best machine model is XLM-R$_Large, which achieves an exact match (EM) score of 85.97% and an F1-score of 88.77% on our dataset. Besides, we analyze experimental results in terms of the question type in Vietnamese and the effect of context on the performance of the MRC models, thereby showing the challenges from the UIT-ViWikiQA dataset that we propose to the natural language processing community.

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