Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring
This work addresses a specific bottleneck in question generation for NLP applications, offering incremental improvements over prior methods.
The paper tackled the problem of generating incorrect question words and irrelevant keywords in sequence-to-sequence models for question generation by introducing a model with sentence-level semantic matching and answer position inferring modules. It achieved state-of-the-art results on SQuAD and MARCO datasets, improving existing models significantly.
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.