Publication: WabiQA: A Wikipedia-Based Thai Question-Answering System
4
Issued Date
2021-01-01
Resource Type
ISSN
03064573
Other identifier(s)
2-s2.0-85096635565
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Information Processing and Management. Vol.58, No.1 (2021)
Suggested Citation
Thanapon Noraset, Lalita Lowphansirikul, Suppawong Tuarob WabiQA: A Wikipedia-Based Thai Question-Answering System. Information Processing and Management. Vol.58, No.1 (2021). doi:10.1016/j.ipm.2020.102431 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76758
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
WabiQA: A Wikipedia-Based Thai Question-Answering System
Other Contributor(s)
Abstract
With vast information that has been digitized and made available online, manually finding the answer to a question can be tedious. While search engines have emerged to facilitate information needs, users would have to manually read through the retrieved articles to locate the answer to a specific question. Therefore, the ability to automatically understand users’ natural language questions and find the correct answers could prove crucial in information retrieval. Indeed, such automatic question-answering solutions have been extensively studied by the natural language processing (NLP) research communities. However, most of the development targets questions and information sources composed in high-resource languages such as English and Chinese. In this paper, we propose WabiQA, a novel system for automatically answering questions in the Thai language using the Thai Wikipedia articles as the knowledge source. Specifically, the proposed method first retrieves the Wikipedia article that is most likely to contain the answer. Then, a bi-directional LSTM model is used to read the article and locate candidate answers, which are ranked by confidence levels and returned to the user. WabiQA won the first prize award from Thailand's National Software Contest 2019 under category “Question-Answering Program from Thai Wikipedia,” with 83.5%, 34.80%, and 45.96%, and outperforming the next best competitors’ systems by 19.99, 24.26, and 33.10 percentage points in terms of Accuracy@1, EM, and F1 respectively. Furthermore, we also develop a prototype mobile application that aims to facilitate Thai users with visual impairment using voice-to-speech technology and an intelligent question-answer categorization. The findings of this research not only expand the horizon of the possibility to develop intelligent NLP applications for the Thai language using only available existing Thai NLP tools, resources, and deep learning technologies, but also shed light on the possibility to apply such techniques to develop many intelligent NLP tasks for the Thai and other low-resource languages such as reading assessment, writing assistance, and entity linking.