With the evolution of pre-trained language models, current open-domain
dialogue systems have achieved great progress in conducting one-session
conversations. In contrast, Multi-Session Conversation (MSC), which consists of
multiple sessions over a long term with the same user, is under-investigated.
In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for
multi-session open-domain dialogue. HAHT maintains a long-term memory of
history conversations and utilizes history information to understand current
conversation context and generate well-informed and context-relevant responses.
Specifically, HAHT first encodes history conversation sessions hierarchically
into a history memory. Then, HAHT leverages historical information to
facilitate the understanding of the current conversation context by encoding
the history memory together with the current context with attention-based
mechanisms. Finally, to explicitly utilize historical information, HAHT uses a
history-aware response generator that switches between a generic vocabulary and
a history-aware vocabulary. Experimental results on a large-scale MSC dataset
suggest that the proposed HAHT model consistently outperforms baseline models.
Human evaluation results support that HAHT generates more human-like,
context-relevant and history-relevant responses than baseline models.

本文提出了历史感知的分层 Transformer - 多轮开放域对话系统，能够利用历史交互信息与时下言语背景生成基于上下文且相关性强的对话回应，并在大规模多轮对话数据集上取得了优异的表现结果。