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Question Generation via Multi-stage Answers Editing Network

Automatic question generation from a sentence or paragraph is a challenging task. Recently, impressive progress has been made, mainly owing to the advance of deep neural networks. However, existing neural approaches tend to simply copy words from the given answer, making the generated question equivocal and informatively redundant. In this work, we propose an edit-based network, named Multi-stage Answers Editing Network (MultiEdit), to generate the question in the three-stage operations. In the initial stage, we convert the answer into an edit vector and then combine the edit vector and sentence representation to initialize the decoder, which helps choose an interrogative word matching the answer type. In the second stage, we edit the contextual representation by computing the weighted average between the context representation and the edit vector. In the final stage, we adopt the additional reinforcement learning to improve the effectiveness of generated semantics and evaluation indicators. Experimental results on both Chinese and English datasets show that our approach outperforms the state-of-the-art methods in terms of both automatic and human evaluation.