Building a Closed-Domain Question Answering System for a Low-Resource Language

Abstract

In recent years, the Question Answering System (QAS) has been widely used to develop many systems, such as conversation systems, chatbots, and intelligent search. Depending on the amount of information or knowledge that the system processes, the system can be applied in answering the questions in an open domain or closed domain. There are many approaches to solving the QA problem, but the neural network models have yielded impressive and promising results, especially the Machine Reading Comprehension approach. In this article, we build a closed-domain QAS for a low-resource language, Vietnamese—specifically, “The Postgraduate Admission of Ho Chi Minh City University of Food Industry, Vietnam.” In addition, we have created two datasets to serve our QAS: vi-SQuAD v1.1, which is automatically translated and edited from SQuAD (Stanford University Question Answering Dataset), and HUFI-PostGrad, which is manually collected. We use two main models for the system, including the Intent Classification model and the Machine Reading Comprehension model. Experimental results initially show that our QAS gives encouraging results.