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。