Context-aware multi-feature fusion for open-domain dialogue generation.

With the rapid development of deep learning, open-domain dialogue models are able to generate relatively fluent conversations. However, current generative models present incomplete representations of dialogue features, which make it prone to generate low-quality or irrelevant-semantic responses. To address this problem, this paper presents a novel context-aware multi-feature fusion model, integrating keywords and category-semantic information into the dialogue generation. Specifically, distinct keywords share various significance in a sentence, incorporating this significance into the conversation text can improve the generative ability of the generative model. Our method enhances the feature representation of the text and thus generates high-quality conversation. Finally, we conducted comprehensive experiments on the DailyDialog dataset and EmpatheticDialogues dataset, analyzing the experimental results and verifying the feasibility of our approach.