Papers with Dialogue discourse parsing
Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)
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| Challenge: | Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality. |
| Approach: | They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue. |
| Outcome: | The proposed framework outperforms baselines on the Molweni and STAC datasets. |
MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing (2024.findings-acl)
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| Challenge: | Existing benchmark datasets for discourse parsing are domain-specific and contain only textual modality . this makes it difficult to accurately understand the dialogue without multi-modal clues . |
| Approach: | They propose a multi-modal Chinese discourse parsing dataset based on open-domain dialogues . they propose to integrate multi-modality into the original textual unimodal DDP model . |
| Outcome: | The proposed dataset improves on the existing unimodal model by adding multimodalities to the model. |
Improving Dialogue Discourse Parsing through Discourse-aware Utterance Clarification (2025.acl-long)
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| Challenge: | Extensive experiments on the STAC and Molweni datasets demonstrate that our approach effectively resolves ambiguities and significantly outperforms the state-of-the-art (SOTA) baselines. |
| Approach: | They propose a Discourse-aware Clarification Module (DCM) that generates clarifications for the parser through systematic clarification type reasoning and discourse goal reasoning. |
| Outcome: | Extensive experiments on the STAC and Molweni datasets demonstrate that the proposed module significantly outperforms the state-of-the-art (SOTA) framework. |
Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)
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| Challenge: | Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. |
| Approach: | They propose to introduce a task-aware paradigm to improve the versatility of the parser. |
| Outcome: | Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the proposed framework. |