Dialogue disentanglement aims to group utterances in a long and
multi-participant dialogue into threads. This is useful for discourse analysis
and downstream applications such as dialogue response selection, where it can
be the first step to construct a clean context/response set. Unfortunately,
labeling all~\emph{reply-to} links takes quadratic effort w.r.t the number of
utterances: an annotator must check all preceding utterances to identify the
one to which the current utterance is a reply. In this paper, we are the first
to propose a~\textbf{zero-shot} dialogue disentanglement solution. Firstly, we
train a model on a multi-participant response selection dataset harvested from
the web which is not annotated; we then apply the trained model to perform
zero-shot dialogue disentanglement. Without any labeled data, our model can
achieve a cluster F1 score of 25. We also fine-tune the model using various
amounts of labeled data. Experiments show that with only 10\% of the data, we
achieve nearly the same performance of using the full dataset\footnote{Code is
released at
https://github.com/chijames/zero_shot_dialogue_disentanglement}.

本文利用无监督学习的方法，解决了多人对话中的话语分组问题，并通过使用海量数据集进行预训练和微调，实现了无需任何有标注的数据就可以达到 25 的聚类 F1 分数。