Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years. The duration of gaps between conversations dictates which topics are relevant and which questions to ask, and dialogue systems which do not explicitly model time may generate responses that are unnatural. In this work we explore the idea of making dialogue models aware of time, and present GapChat, a multi-session dialogue dataset in which the time between each session varies. While the dataset is constructed in real-time, progress on events in speakers' lives is simulated in order to create realistic dialogues occurring across a long timespan. We expose time information to the model and compare different representations of time and event progress. In human evaluation we show that time-aware models perform better in metrics that judge the relevance of the chosen topics and the information gained from the conversation.

翻译：了解如何在时间推移中结束和恢复对话是交流的自然组成部分，使得讨论可以跨越数周、数月或数年。对话间隔的时长决定了哪些话题相关以及应提出哪些问题，而未能显式建模时间的对话系统可能生成不自然的回复。在本研究中，我们探索使对话模型感知时间的概念，并提出 GapChat——一个多会话对话数据集，其中每次会话之间的时间间隔各不相同。尽管该数据集是实时构建的，但通过模拟说话者生活中事件的进展，来创建跨越较长时间的逼真对话。我们将时间信息暴露给模型，并比较不同的时间与事件进展表示。在人工评估中，我们展示了时间感知模型在评判所选话题相关性和对话信息获取量的指标上表现更优。