Learning To Generate Diverse Questions From Keywords

Diverse text generation has been emerging as an important topic of natural language generation. Traditional studies on question generation mainly investigate how to generate one question based on a given input (one-to-one). In this paper, we focus on a more complex question generation task, i.e., generating a series of questions for each set of keywords (one-to-many). As an effort towards this, we propose a novel neural generative model, which incorporates context information and control signal to produce multiple diverse questions from a given fixed set of keywords. The control signal is designed to increase the diversity of questions by capturing the diverse patterns from the entire dataset. The context information is used to guarantee the generated questions are highly related to the given keywords. To evaluate the effectiveness of the proposed model, we collect a dataset which contains 62835 questions with respect to 12567 sets of keywords. To the best of our knowledge, it’s the first Chinese financial dataset for diverse question generation. The experimental results show that our model outperforms the competitor methods in terms of BLEU and Distinct. The qualitative evaluation indicates that our model is able to generate diverse and meaningful questions.