Dialogue disentanglement aims to detach the chronologically ordered
utterances into several independent sessions. Conversation utterances are
essentially organized and described by the underlying discourse, and thus
dialogue disentanglement requires the full understanding and harnessing of the
intrinsic discourse attribute. In this paper, we propose enhancing dialogue
disentanglement by taking full advantage of the dialogue discourse
characteristics. First of all, \textbf{in feature encoding stage}, we construct
the heterogeneous graph representations to model the various dialogue-specific
discourse structural features, including the static speaker-role structures
(i.e., speaker-utterance and speaker-mentioning structure) and the dynamic
contextual structures (i.e., the utterance-distance and partial-replying
structure). We then develop a structure-aware framework to integrate the rich
structural features for better modeling the conversational semantic context.
Second, \textbf{in model learning stage}, we perform optimization with a
hierarchical ranking loss mechanism, which groups dialogue utterances into
different discourse levels and carries training covering pair-wise and
session-wise levels hierarchically. Third, \textbf{in inference stage}, we
devise an easy-first decoding algorithm, which performs utterance pairing under
the easy-to-hard manner with a global context, breaking the constraint of
traditional sequential decoding order. On two benchmark datasets, our overall
system achieves new state-of-the-art performances on all evaluations. In-depth
analyses further demonstrate the efficacy of each proposed idea and also reveal
how our methods help advance the task. Our work has great potential to
facilitate broader multi-party multi-thread dialogue applications.

本文提出了一种基于异构图表示的对话解缠方法，构建了不同的讨论结构特征模型，包括静态发言角色结构和动态背景结构，并采用层级排名损失和易于首先解码算法来实现话语理解和分离。

重新审视话语论以进行对话分离

Revisiting Conversation Discourse for Dialogue Disentanglement

A huge number of multi-participant dialogues happen online every day, which
leads to difficulty in understanding the nature of dialogue dynamics for both
humans and machines. Dialogue disentanglement aims at separating an entangled
dialogue into detached sessions, thus increasing the readability of long
disordered dialogue. Previous studies mainly focus on message-pair
classification and clustering in two-step methods, which cannot guarantee the
whole clustering performance in a dialogue. To address this challenge, we
propose a simple yet effective model named CluCDD, which aggregates utterances
by contrastive learning. More specifically, our model pulls utterances in the
same session together and pushes away utterances in different ones. Then a
clustering method is adopted to generate predicted clustering labels.
Comprehensive experiments conducted on the Movie Dialogue dataset and IRC
dataset demonstrate that our model achieves a new state-of-the-art result.

提出了一种名为 CluCDD 的模型，该模型通过对话分离技术和对比学习聚合话语，以生成预测聚类标签并取得最新的最优结果。

CluCDD: 基于聚类的对话解缠方法

CluCDD:Contrastive Dialogue Disentanglement via Clustering

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。