对话编年史：迈向多轮对话中多样的时间与关系动态
Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
AI总结：
本文提出多轮对话数据集Conversation Chronicles，结合时间间隔与说话者关系，并设计约630M参数的对话模型ReBot，实现长期上下文理解与高人类参与度。
摘要：
在自然语言处理领域，开放域聊天机器人已成为一个重要的研究课题。然而，现有开放域聊天机器人研究的一个主要局限是其单一关注短期的单轮对话，忽略了在当前对话之前的多个连续轮次中理解上下文信息的潜在需求。在构成多轮对话设置上下文的要素中，轮次之间的时间间隔和说话者之间的关系尤为重要。尽管它们很重要，但目前的研究工作尚未充分解决这些对话组成部分。在本文中，我们引入了一个新的1M多轮对话数据集，称为Conversation Chronicles，用于实现一个结合了时间间隔和细粒度说话者关系的长期对话设置。遵循近期的工作，我们利用大型语言模型来生成数据。广泛的人工评估表明，Conversation Chronicles中的对话片段反映了这些特性，同时在所有轮次中保持连贯和一致的交互。我们还提出了一个对话模型，称为ReBot，它由按时间顺序的摘要和对话生成模块组成，仅使用约630M参数。在Conversation Chronicles上训练时，ReBot展示了对长期上下文的理解，并获得了高人类参与度评分。
英文摘要：
In the field of natural language processing, open-domain chatbots have emerged as an important research topic. However, a major limitation of existing open-domain chatbot research is its singular focus on short single-session dialogue, neglecting the potential need for understanding contextual information in multiple consecutive sessions that precede an ongoing dialogue. Among the elements that compose the context in multi-session conversation settings, the time intervals between sessions and the relationships between speakers would be particularly important. Despite their importance, current research efforts have not sufficiently addressed these dialogical components. In this paper, we introduce a new 1M multi-session dialogue dataset, called Conversation Chronicles, for implementing a long-term conversation setup in which time intervals and fine-grained speaker relationships are incorporated. Following recent works, we exploit a large language model to produce the data. The extensive human evaluation shows that dialogue episodes in Conversation Chronicles reflect those properties while maintaining coherent and consistent interactions across all the sessions. We also propose a dialogue model, called ReBot, which consists of chronological summarization and dialogue generation modules using only around 630M parameters. When trained on Conversation Chronicles, ReBot demonstrates long-term context understanding with a high human engagement score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
