VLSP 2021 Shared Task: Vietnamese Machine Reading Comprehension

• URL: http://arxiv.org/abs/2203.11400v1
• Date: Tue, 22 Mar 2022 00:44:41 GMT
• Title: VLSP 2021 Shared Task: Vietnamese Machine Reading Comprehension
• Authors: Kiet Van Nguyen, Son Quoc Tran, Luan Thanh Nguyen, Tin Van Huynh, Son T. Luu, Ngan Luu-Thuy Nguyen
• Abstract summary: This article presents details of the organization of the shared task, an overview of the methods employed by shared-task participants, and the results. We provide a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. The UIT-ViQuAD 2.0 dataset motivates more researchers to explore Vietnamese machine reading comprehension, question answering, and question generation.
• Score: 2.348805691644086
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the shared task on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the shared task, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% EM and 67.43% F1-score on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model using the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates more researchers to explore Vietnamese machine reading comprehension, question answering, and question generation.

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