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.

我们通过构建一个模拟时间变化的多次对话数据集 GapChat，研究了如何使对话模型意识到时间，并比较了不同的时间和事件进展表示方法。通过人工评估，我们证明了时间意识模型在选择话题的相关性和从对话中获得的信息方面表现更好。

提升长期对话生成中对话之间的差距

Mind the Gap Between Conversations for Improved Long-Term Dialogue  Generation

Modeling and understanding time remains a challenge in contemporary video
understanding models. With language emerging as a key driver towards powerful
generalization, it is imperative for foundational video-language models to have
a sense of time. In this paper, we consider a specific aspect of temporal
understanding: consistency of time order as elicited by before/after relations.
We establish that six existing video-language models struggle to understand
even such simple temporal relations. We then question whether it is feasible to
equip these foundational models with temporal awareness without re-training
them from scratch. Towards this, we propose a temporal adaptation recipe on top
of one such model, VideoCLIP, based on post-pretraining on a small amount of
video-text data. We conduct a zero-shot evaluation of the adapted models on six
datasets for three downstream tasks which require a varying degree of time
awareness. We observe encouraging performance gains especially when the task
needs higher time awareness. Our work serves as a first step towards probing
and instilling a sense of time in existing video-language models without the
need for data and compute-intense training from scratch.

本文旨在向已有视频语言模型注入时间感，通过后预训练等方式实现，观察经过处理后的模型在时间感知任务中的性能提升情况，并在六个数据集上进行了零样本评估。