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arXiv · 1912.00879

Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring

Xiyao Ma·Qile Zhu·Yanlin Zhou·Xiaolin Li·Dapeng Wu
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Abstract

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.

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Xiyao Ma, Qile Zhu, Yanlin Zhou, Xiaolin Li, Dapeng Wu. 2020-02-03. Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring. https://arxiv.org/abs/1912.00879

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