The open-domain conversation generation task aims to generate contextually relevant and informative responses based on a given conversation history. A critical challenge in open-domain dialogs is the tendency of models to generate safe responses. Existing work has often incorporated keyword information in the conversation history for response generation to relieve this problem. However, these approaches interact weakly between responses and keywords or ignore the association between keyword extraction and conversation generation. In this paper, we propose a method based on a Keyword-Aware Transformers Network (KAT) that can fuse contextual keywords. Specifically, the model enables keywords and contexts to fully interact with responses for keyword semantic enhancement. We jointly model the keyword extraction task and the dialog generation task in a multi-task learning fashion. Experimental results of two Chinese open-domain dialogue datasets showed that our proposed model outperformed the methods in both semantic and non-semantic evaluation metrics, improving Coherence, Fluency, and Informativeness in manual evaluation.

No abstract