This paper explores the task of answer-aware questions generation. Based on the attentionbased pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multitask learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.
REF exploit language modeling as an auxiliary task to assist question generation task by multi-task learning, which motivates our work greatly.
Multi-Task Learning with Language Modeling for Question Generation
ArXiv
ArXiv
Computer Science