ReBoost: a retrieval-boosted sequence-to-sequence model for neural response generation

Human–computer conversation is an active research topic in natural language processing. One of the representative methods to build conversation systems uses the sequence-to-sequence (Seq2seq) model through neural networks. However, with limited input information, the Seq2seq model tends to generate meaningless and trivial responses. It can be greatly enhanced if more supplementary information is provided in the generation process. In this work, we propose to utilize retrieved responses to boost the Seq2seq model for generating more informative replies. Our method, called ReBoost, incorporates retrieved results in the Seq2seq model by a hierarchical structure. The input message and retrieved results can influence the generation process jointly. Experiments on two benchmark datasets demonstrate that our model is able to generate more informative responses in both automatic and human evaluations and outperforms the state-of-the-art response generation models.