Dynamic Working Memory for Context-Aware Response Generation

In human-to-human conversations, the context generally provides several backgrounds and strategic points for the following response. Therefore, many response generation approaches have explored the methodologies to incorporate the context into the encoder-decoder architecture, to generate context-aware responses that are remarkably relevant and cohesive to the given context. However, most approaches pay less attention to semantic interactions implicitly existing within contextual utterances, which are of great importance to capture semantic clues of the given dialog context, indeed. This paper proposes a dynamic working memory mechanism to model long-term semantic hints in the conversation context, by performing semantic interactions between utterances and updating context representation dynamically. Then, the outputs of the dynamic working memory are employed to provide helpful clues for the encoder-decoder architecture to generate responses to the given dialog. We have evaluated the proposed approach on Twitter Customer Service Corpus and OpenSubtitles Corpus, with several automatic evaluation metrics and the human evaluation, and the empirical results show the effectiveness of the proposed method.