Open-domain long-term memory conversation can establish long-term intimacy with humans, and the key is the ability to understand and memorize long-term dialogue history information. Existing works integrate multiple models for modelling through a pipeline, which ignores the coupling between different stages. In this paper, we propose a Unified framework for Long-term Memory Conversations (UniMC), which increases the connection between different stages by learning relevance representation. Specifically, we decompose the main task into three subtasks based on probability graphs: 1) conversation summarization, 2) memory retrieval, 3) memory-augmented generation. Each subtask involves learning a representation for calculating the relevance between the query and memory, which is modelled by inserting a special token at the beginning of the decoder input. The relevance representation learning strengthens the connection across subtasks through parameter sharing and joint training. Extensive experimental results show that the proposed method consistently improves over strong baselines and yields better dialogue consistency and engagingness.

翻译：开放式长期记忆对话能够与人类建立长期亲密关系，其关键在于理解和记忆长期对话历史信息的能力。现有工作通常通过流水线方式集成多个模型进行建模，忽略了不同阶段之间的耦合性。本文提出了一种面向长期记忆对话的统一框架（UniMC），通过学习相关性表示来增强不同阶段间的关联性。具体而言，我们基于概率图将主要任务分解为三个子任务：1）对话摘要生成，2）记忆检索，3）记忆增强生成。每个子任务均需学习一种表示，用于计算查询与记忆之间的相关性，该表示通过在解码器输入起始位置插入特殊标记进行建模。相关性表示学习通过参数共享和联合训练强化了各子任务间的联系。大量实验结果表明，所提方法相比强基线模型持续取得提升，并能产生更好的对话一致性与参与度。