| Challenge: | Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. |
| Approach: | They propose a speaker-aware model for parsing on multi-party dialogues using interaction features between different speakers. |
| Outcome: | The proposed model achieves the best-reported performance on two standard benchmark datasets. |
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Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset (2026.findings-acl)
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| Challenge: | Existing studies on multi-party dialogue discourse parsing focus on textual modality and two-party dialog . et al., 2016) focused on text-based discourse parses, ignoring the complexity and richness of multimodal interactions in real-world scenarios. |
| Approach: | They construct the first publicly available English multimodal dataset for multi-party dialogue discourse parsing based on American TV dramas. |
| Outcome: | The proposed dataset contains 495 dialogue segments with 6,374 utterances and 9.1 hours of parallel video content, covering rich multi-party interaction scenarios. |
Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)
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| Challenge: | Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. |
| Approach: | They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation. |
| Outcome: | The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches. |
Enhancing Multi-party Dialogue Discourse Parsing with Explanation Generation (2025.coling-main)
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| Challenge: | Multi-party dialogue discourse parsing is an important and challenging task in natural language processing. |
| Approach: | They propose a model to integrate external knowledge from Large Language Models to analyze dialogue discourse structures and semantic relations between utterances in multi-party conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) models on two public datasets. |
Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision (2022.coling-1)
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| Challenge: | Existing models for textual dialogues do not include speaker annotations. |
| Approach: | They propose a speaker clustering model for textual dialogues that groups utterances without annotations so that the actual speakers are identical inside each cluster. |
| Outcome: | The proposed model outperforms the sequence classification baseline and benefits from the auxiliary dialogue act classification task. |
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)
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| Challenge: | Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics. |
| Approach: | They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks at two benchmarks. |
Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization (2023.findings-acl)
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| Challenge: | Current speaker diarization systems consider only acoustic information, resulting in performance degradation when encountering adverse acustic environment. |
| Approach: | They propose methods to extract speaker-related information from conversational semantics in multi-party meetings. |
| Outcome: | The proposed method improves on AISHELL-4 and AliMeeting datasets on speakers diarization and speaker-turn detection. |
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for multi-party dialogue machine reading comprehension focus on how to incorporate speaker information into the model, which is usually rare in real scenarios. |
| Approach: | They propose to model speaker and key-utterances using self-supervised prediction tasks and capture salient clues in a long dialogue. |
| Outcome: | The proposed method outperforms baseline models and state-of-the-art models on two benchmark datasets. |
Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis (2022.emnlp-main)
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| Challenge: | Existing approaches to speaker diarization treat speaker dependency and overlaps as multi-label classification problems. |
| Approach: | They propose to reformulate overlapped speaker diarization task as a single-label prediction problem via power set encoding (PSE) to overcome the disadvantages, they propose a speaker overlap-aware neural diarisation model which incorporates a context-independent scorer and a contextual-dependent score. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on speaker voice activity detection and improves relative diarization error reduction by 6.30%. |
PACHAT: Persona-Aware Speech Assistant for Multi-party Dialogue (2025.emnlp-main)
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| Challenge: | Extensive research on spoken dialogue systems has advanced the development of intelligent voice assistants, but integration of role information within speech remains an underexplored area. |
| Approach: | They propose a language-based spoken dialogue system that integrates role information within speech to generate contextually appropriate responses. |
| Outcome: | The proposed architecture achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems. |