Dialogue generation, as a crucial subtask of dialogue systems, is garnering increasing attention in the field of Natural Language Processing (NLP). The success of dialogue generation relies on effectively utilizing context information to ensure coherent and diverse responses. However, current approaches heavily rely on external sources rather than leveraging the inherent dialogue content. We propose a new approach to address this challenge by introducing semantic segmentation from the field of image processing into NLP. Our contribution lies in the development of a Dialogue Generation model with Hierarchical Encoding and Semantic segmentation of dialogue Context, which is called DGHESC. This model is topic and speaker-aware, capturing the flow of topic and speaker information within the dialogue context using a hierarchical transformer-based framework. Specifically, we extract semantic information at the word-level for each utterance, segment the dialogue context based on topic and speaker semantics, and employ attention mechanisms to model the context at the utterance-level. Experimental results on two open-domain datasets demonstrate the effectiveness of DGHESC. It enhances response quality and achieves state-of-the-art performances on the datasets.

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.