2020/01/01 by Kiet Van Nguyen, Khiem Vinh Tran, Son T. Luu +2 · 1 citation
Computer Science · #Artificial intelligence #Baseline (sea) #Benchmark (surveying) #Comprehension #Computer science #Construct (python library) #Linguistics #Machine translation #Natural Language Processing Techniques #Natural language processing #Question answering #Reading (process) #Reading comprehension #Similarity (geometry) #Task (project management) #Text Readability and Simplification #Topic Modeling #Vietnamese #cs.CL
paper · pdf · doi:10.1109/access.2020.3035701
published as IEEE Access, 2020
openalex publication_date 2020/01/01 · openalex created_date 2020/05/21 · arxiv created 2020/11/01 · arxiv updated 2020/11/03 · openalex updated_date 2026/08/05
Although Vietnamese is the 17thmost 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.