Year: 2019
Type: preprint
Abstract: This paper explores the task of answer-aware questions generation. Based on the attention-based 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 multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.
Source: arXiv (Cornell University)
Authors: Zhou, Wenjie, Zhang, Minghua, Wu, Yunfang
Institution: Peking University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: 句法语义分析与开放域信息抽取融合技术研究
OpenAlex ID: W2970566942