Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension

Abstract

Although Vietnamese is the 17th most popular native-speaker language in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexical-based MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website for research purposes.

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Cited by in corpus (7)

• VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension
• A Vietnamese Dataset for Evaluating Machine Reading Comprehension
• Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
• ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text
• An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension
• Sentence Extraction-Based Machine Reading Comprehension for Vietnamese
• Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization