  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.
