Towards Vietnamese Question and Answer Generation: An Empirical Study

Abstract

Question-answer generation (QAG) is a challenging task that generates both questions and answers from a given input paragraph context. The QAG task has recently achieved promising results thanks to the appearance of large pre-trained language models, yet, QAG models are mainly implemented in common languages, e.g., English. There still remains a gap in domain and language adaptation of these QAG models to low-resource languages such as Vietnamese. To address the gap, this article presents a large-scale and systematic study of QAG in Vietnamese. To do that, we first implement several QAG models by using the common fine-tuning techniques based on powerful pre-trained language models. We next introduce a set of instructions designed for the QAG task. These instructions are used to fine-tuned the pre-trained language and large language models. Extensive experimental results of both automatic and human evaluation on five benchmark machine reading comprehension datasets show two important points. First, the instruction-tuning method has the potential to enhance the performance of QAG models. Second, large language models trained in English need more data for fine-tuning to work well on the downstream QAG tasks of low-resource languages. We also provide a prototype system to demonstrate how our QAG models actually work. The code for fine-tuning QAG models and instructions are also made available.