Increasing naturalness of human–machine dialogue: The users’ choices inference of options in machine-raised questions

In many practical applications, the machine needs to actively ask humans to obtain their intents. The process that the machine raises questions and users return answers is called reverse QA, which is an important part of a human–machine dialogue. However, in many dialogue systems, the machine restricts users from answering questions by clicking on option items, which is unnatural and restricted. In addition, this method may lose important information expressed by users. Users should be allowed to answer questions in natural language in a more natural and intelligent dialogue system. To obtain users’ intents, users’ choices of questions’ options must be inferred from their answers. In this paper, we propose an advanced answer understanding network (UCINet) which infers users’ choices of options in machine-raised questions accurately and efficiently according to the users’ answer. Furthermore, metric learning is introduced for the model to learn better text representations. Based on the assumption that texts are determined by both semantics and styles, we propose a style-based answer generation network (SAGNet) which can generate various answers with different styles for a question. The generated answers are used to achieve data augmentation for UCINet’s training. Experimental results on two reverse QA data sets demonstrate that UCINet achieves impressive results compared to other strong competitors. Using SAGNet for answer generation, we obtain answers with various styles and good quality. Our work can be widely used in intelligent customer service, mobile phone assistants, and other human–machine dialogue systems.