TY - JOUR AU - Ma, Xiyao AU - Zhu, Qile AU - Zhou, Yanlin AU - Li, Xiaolin PY - 2020/04/03 Y2 - 2024/03/28 TI - Improving Question Generation with Sentence-Level Semantic Matching and Answer Position Inferring JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 05 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v34i05.6366 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6366 SP - 8464-8471 AB - <p>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 general 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.</p> ER -