  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, 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, 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, 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.
