Topic and Speaker-aware Hierarchical Encoder-Decoder Model for Dialogue Generation

As one of the most common social behavior in human society, communication in multi-turn conversation or dialogue system has always been a research focuses of natural language processing (NLP).The quality of downstream tasks in multi-turn dialogue is often determined by the result of dialogue context modeling.For dialogue generation, the context information will determine the consistency and diversity of the generated responses.However, the current research on dialogue generation increasingly relies on external information rather than mining from the dialogue content itself.In this paper, we propose a topic and speaker-aware hierarchical encoder-decoder (TSHED) model to capture the topic and speaker information flow in the context for response generation with the hierarchical transformer-based framework.Specifically, we obtain semantic information of each utterance at word-level and then apply topic and speaker-aware attention to model context at utterancelevel.Experimental results on two open-domain datasets show that TSHED significantly improves the quality of responses and outperforms strong baselines.