Order-Sensitive Keywords Based Response Generation in Open-Domain Conversational Systems

External keywords are crucial for response generation models to address the generic response problems in open-domain conversational systems. The occurrence of keywords in a response depends heavily on the order of the keywords as they are generated sequentially. Meanwhile, the order of keywords also affects the semantics of a response. Previous keywords based methods mainly focus on the composite of keywords, while the order of keywords has not been sufficiently discussed. In this work, we propose an order-sensitive keywords based model to explore the influence of the order of keywords in open-domain response generation. It automatically inferences the most suitable order that is optimized to generate a natural and relevant response, and subsequently generates the response using the ordered keywords as building blocks. We conducted experiments on a public Twitter dataset and the results show that our approach outperforms the state-of-the-art baselines in both automatic and human evaluations.