Mind the Gap Between Conversations for Improved Long-Term Dialogue
  Generation
Qiang Zhang Jason Naradowsky Yusuke Miyao
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.