Papers › Improving Multi-Party Dialogue Discourse Parsing via Domain Integration
Improving Multi-Party Dialogue Discourse Parsing via Domain Integration
Zhengyuan Liu, Nancy F. Chen
While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns, and dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units, and provide feature-rich structural information for downstream tasks. However, the existing corpora with dialogue discourse annotation are collected from specific domains with limited sample sizes, rendering the performance of data-driven approaches poor on incoming dialogues without any domain adaptation. In this paper, we first introduce a Transformer-based parser, and assess its cross-domain performance. We next adopt three methods to gain domain integration from both data and language modeling perspectives to improve the generalization capability. Empirical results show that the neural parser can benefit from our proposed methods, and performs better on cross-domain dialogue samples.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Discourse Parsing | Molweni | Hierarchical | Link & Rel F1 | 56.1 | #6 of 7 | Archive leaderboard | report |
| Discourse Parsing | Molweni | Hierarchical | Link F1 | 80.1 | #6 of 7 | Archive leaderboard | report |
| Discourse Parsing | STAC | Hierarchical | Link & Rel F1 | 57.2 | #4 of 7 | Archive leaderboard | report |
| Discourse Parsing | STAC | Hierarchical | Link F1 | 75.5 | #4 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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