Improving Question Generation With Sentence-level Semantic Matching And Answer Position Inferring
Xiyao Ma, Qile Zhu, Yanlin Zhou, Xiaolin Li, Dapeng Wu . Proceedings of the AAAI Conference on Artificial Intelligence 2020 – 51 citations
[Paper]
Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two concrete modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.