A Recurrent BERT-based Model for Question Generation

Ying-Hong Chan, Yao-Chung Fan


Abstract
In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce three 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. Accordingly, we propose another two models by restructuring our BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the recent question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of the existing best models from 16.85 to 22.17.
Anthology ID:
D19-5821
Volume:
Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
154–162
Language:
URL:
https://aclanthology.org/D19-5821
DOI:
10.18653/v1/D19-5821
Bibkey:
Cite (ACL):
Ying-Hong Chan and Yao-Chung Fan. 2019. A Recurrent BERT-based Model for Question Generation. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 154–162, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Recurrent BERT-based Model for Question Generation (Chan & Fan, EMNLP 2019)
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PDF:
https://aclanthology.org/D19-5821.pdf