BERT for Question Generation
Ying-Hong Chan, Yao-Chung Fan · 2019
In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks.We introduce two neural architectures built on top of BERT for question generation tasks.The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation.And, the second one remedies the first one by restructuring the BERT employment into a sequential manner for taking information from previous decoded results.Our models are trained and evaluated on the question-answering dataset SQuAD.Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of existing best models from 16.85 to 21.04.