论文标题
越南多项选择阅读理解的深神经网络模型的实验研究
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension
论文作者
论文摘要
机器阅读理解（MRC）是自然语言处理中的一项具有挑战性的任务，它使计算机理解自然语言文本并根据这些文本回答问题。解决此问题的技术有许多技术，单词表示是一种非常重要的技术，它对流行语言（例如英语和中文）的机器阅读理解问题的准确性最大。但是，很少有关于MRC的低资源语言（例如越南语）进行的研究。在本文中，我们对基于神经网络的模型进行了几项实验，以了解单词表示对越南多项选择机的阅读理解的影响。我们的实验包括在六个不同的越南单词嵌入方式和BERT模型上使用共同匹配模型，以进行多项选择阅读理解。在VIMMRC语料库上，测试集的BERT模型的准确性为61.28％。
Machine reading comprehension (MRC) is a challenging task in natural language processing that makes computers understanding natural language texts and answer questions based on those texts. There are many techniques for solving this problems, and word representation is a very important technique that impact most to the accuracy of machine reading comprehension problem in the popular languages like English and Chinese. However, few studies on MRC have been conducted in low-resource languages such as Vietnamese. In this paper, we conduct several experiments on neural network-based model to understand the impact of word representation to the Vietnamese multiple-choice machine reading comprehension. Our experiments include using the Co-match model on six different Vietnamese word embeddings and the BERT model for multiple-choice reading comprehension. On the ViMMRC corpus, the accuracy of BERT model is 61.28% on test set.
