ViMRC - VLSP 2021: Context-Aware Answer Extraction in Vietnamese Question Answering

Thi-Thu-Hong Le
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Abstract

MRC is challenging the natural language processing fields; machines automatically have to answer questions based on specific passages for this task. In recent years, machine reading comprehension (MRC) has received much attention; many articles have been written about this task. However, most of those articles only develop models in two main languages, English and Chinese. In this article, we propose to apply a new model to the task of reading comprehension in Vietnamese. Specifically, we use BLANC (BLock AttentioN for Context prediction) on pre-trained baseline models to solve the Machine reading comprehension (MRC) task on Vietnamese. We have achieved good results when using BLANC on the baseline model. Specifically, with the MRC task at the VLSP share-task 2021, we scored 76.877% of F1-score on the private test and ranked 2nd in the total. This shows that BLANC works very well in MRC tasks and further enhances the Vietnamese MRC development.
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ViMRC - VLSP 2021:越南语问答中的上下文感知答案提取
MRC正在挑战自然语言处理领域;为了完成这项任务,机器必须根据特定的段落自动回答问题。近年来,机器阅读理解(MRC)备受关注;关于这个任务已经写了很多文章。然而,这些文章大多只开发了两种主要语言的模型,英语和中文。在本文中,我们提出了一种新的模式应用于越南语阅读理解任务。具体来说,我们在预训练的基线模型上使用BLANC (BLock AttentioN for Context prediction)来解决越南语的机器阅读理解(MRC)任务。我们在基线模型上使用BLANC取得了很好的效果。具体来说,在VLSP共享任务2021的MRC任务中,我们在私测中得分为f1的76.877%,排名第二。这表明BLANC在MRC任务中工作得很好,并进一步促进了越南MRC的发展。
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