Question Generation Based Product Information

Abstract

With the continuous development of the Internet, the field of e-commerce generates many comments on products. It is of great significance for both merchants and customers to generate product-related questions by utilizing a large amount of review information of products. In order to get rid of the traditional constraints of generating models based on artificial rules and make the question generation more accurate, this paper proposes a question generation model based on product information. Compared with the existing approaches, this model can generate questions more relevant to the products, and more fluent. In particular, the model can not only avoid the problem that the vocabulary exceeds the dictionary, but also extract the vocabulary needed for question generation from the original text and the dictionary. The experimental results show that in the task of generating short text based on comments, compared with the existing neural network model, the effectiveness has been greatly improved.