Topic-aware dialogue generation with two-hop based graph attention

Generating on-topic responses and understanding the background information of context are both significant for dialogue generation. However, few works simultaneously concentrate on these two issues. For this purpose, we propose an open-domain topic-aware dialogue generation model via joint learning. We first design two-hop based static graph attention mechanism to enhance the semantic representations of context, and then two auxiliary sub-tasks are introduced. Topic Predictor module is designed to focus on the most pertinent topics and Language Modeling module further facilitates learning richer information from context. Experimental study has shown the proposed model’s promising potential. In particular, our model predicts the most topics that best match the query per response. Besides, further analysis proves that our model can generate more diversified and informative responses.