Publication Type
Journal Article
Version
acceptedVersion
Publication Date
11-2024
Abstract
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 article, 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 pairwise 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.
Keywords
Dialogue disentanglement, Graph neural network, Feature encoding, Model learning
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Research Areas
Intelligent Systems and Optimization
Publication
ACM Transactions on Information Systems
Volume
43
Issue
1
First Page
1
Last Page
34
ISSN
1046-8188
Identifier
10.1145/3698191
Publisher
ACM
Citation
LI, Bobo; FEI, Hao; LI, Fei; WU, Shengqiong; LIAO, Lizi; WEI, Yinwei; CHUA, Tat-seng; and JI, Donghong.
Revisiting conversation discourse for dialogue disentanglement. (2024). ACM Transactions on Information Systems. 43, (1), 1-34.
Available at: https://ink.library.smu.edu.sg/sis_research/9883
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3698191
