Exemplar encoder-decoder for neural conversation generation

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize similar examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model. The retrieved responses are used to create exemplar vectors that are used by the decoder to generate the response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. We present detailed experiments on two large data sets and find that our method outperforms state of the art sequence to sequence generative models on several recently proposed evaluation metrics. We also observe that the responses generated by the proposed EED model are more informative and diverse compared to existing state-of-the-art method.