NER2QUES: combining named entity recognition and sequence to sequence to automatically generating Vietnamese questions

Named entity recognition (NER) is an important task in natural language processing. NER is usually used to classify documents, extract information, and translate languages. However, few studies have used NER types to automatically generate questions. In this paper, we proposed a method named NER2QUES to solve the above problem for a low-resource language such as Vietnamese. NER2QUES was the combining pre-trained language model and sequence-to-sequence model. Specifically, we used BERT to detect NERs in a sentence and then applied a sequence-to-sequence model to automatically generate questions that corresponded to NER’s types. We compared the accuracy of the proposed method to PhoBERT and spaCy on the NER task. Also, we used F1, BLEU, ROUGE, and METEOR to measure the effectiveness of this approach with the rules-based method, T5, and BERT on question generation tasks. The experiment results show that the accuracy of our method is more improved than previous methods’ accuracy of 94% on SQuAD, 89% on XQuAD, and 95% on MLQA. This indicates that using NER to automatically generate questions may enrich question answering systems. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.