  Pre-training on large-scale open-domain dialogue data can substantially
improve the performance of dialogue models. However, the pre-trained dialogue
model's ability to utilize long-range context is limited due to the scarcity of
long-turn dialogue sessions. Most dialogues in existing pre-training corpora
contain fewer than three turns of dialogue. To alleviate this issue, we propose
the Retrieve, Reorganize and Rescale framework (Re$^3$Dial), which can
automatically construct billion-scale long-turn dialogues by reorganizing
existing short-turn ones. Given a short-turn session, Re$^3$Dial first employs
a session retriever to retrieve coherent consecutive sessions. To this end, we
train the retriever to capture semantic and discourse relations within
multi-turn dialogues through contrastive training. Next, Re$^3$Dial samples a
session from retrieved results following a diversity sampling strategy, which
is designed to penalize repetitive or generic sessions. A longer session is
then derived by concatenating the original session and the sampled session. By
repeating the above process, Re$^3$Dial can yield a coherent long-turn
dialogue. Extensive experiments on multiple multi-turn dialogue benchmarks
demonstrate that Re$^3$Dial significantly improves the dialogue model's ability
to utilize long-range context and thus generate more sensible and informative
responses. Finally, we build a toolkit for efficiently rescaling conversations
with Re$^3$Dial, which enables us to construct a corpus containing 1B Chinese
dialogue sessions with 11.3 turns on average (5$\times$ longer than the
original corpus). Our retriever model, code, and data is publicly available at
\url{https://github.com/thu-coai/Re3Dial}.
