A question generation model based on semantic enhancement and reinforcement learning

Abstract

The purpose of the Question Generation (QG) task is to automatically generate a reasonable and relevant question for the given document, the goal of this paper is to improve the performance of the question generation model and thus generate higher quality questions. This paper mainly focuses on the neural network model of Seq2Seq architecture and aims to two problems existed: exposure bias caused by the difference between the training and testing processes of the current Seq2Seq architecture model, and insufficient utilization of semantic information of the input documents. This paper proposes a question generation method based on semantic enhancement and reinforcement learning, which enhances the document representation through semantic graph; Add the reinforcement learning framework, use three special indicators for question generation tasks to fine-tune the model parameters during the training process to improve the performance of the model. The experiments’ results on show that this method effectively improves the quality of generating questions.