| Challenge: | In multi-party chat, it is common for multiple conversations to occur concurrently . a new model that automatically disentangles conversation threads is proposed . |
| Approach: | They propose a Context-Aware Thread Detection model that automatically disentangles conversation threads in chat logs. |
| Outcome: | The proposed model outperforms state-of-the-art models on four real-world chat logs. |
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Unsupervised Conversation Disentanglement through Co-Training (2021.emnlp-main)
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| Challenge: | Existing work on conversation disentanglement relies heavily on human annotations, which is expensive to obtain in practice. |
| Approach: | They propose to train a conversation disentanglement model without referencing human annotations . they use a message-pair classifier and a session classifier to retrieve local relations . |
| Outcome: | The proposed method achieves competitive performance compared to previous methods on a large movie dialogue dataset. |
Speaker-Aware Discourse Parsing on Multi-Party Dialogues (2022.coling-1)
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| 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. |
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. |
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking (2020.emnlp-main)
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| Challenge: | Existing response selection methods focus on a two-party single-conversation scenario. |
| Approach: | They propose a multi-task learning framework that frames response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. |
| Outcome: | The proposed framework outperforms existing methods on an Ubuntu IRC dataset in response selection and topic disentanglement tasks. |
Structural Characterization for Dialogue Disentanglement (2022.acl-long)
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| Challenge: | tangled multi-party dialogues lead to difficulties in understanding the dialogue history for both human and machine. |
| Approach: | They propose a model for disentangling multi-party dialogues using speaker property and reference dependency. |
| Outcome: | The proposed model achieves state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension. |
Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations (2024.emnlp-main)
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| Challenge: | Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. |
| Approach: | They propose a methodological pipeline to investigate model performance across structural attributes of conversations. |
| Outcome: | The proposed method analyzes the performance of an LLM to classify multi-party conversations . it shows that response selection relies more on the textual content of conversations compared to addressee recognition . |
Dramatic Conversation Disentanglement (2023.findings-acl)
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| Challenge: | a new dataset is available for studying conversation disentanglement in movies and TV series . a recent study focused on IRC chatroom dialogues, but movies and television show provide a space for study . |
| Approach: | They propose a dataset for studying conversation disentanglement in movies and TV series . they operationalize a conversational thread and apply the best-performing model to 808 movies . |
| Outcome: | The proposed model disentangles 808 movies from 10,033 dialogue turns . the best-performing model is compared with previous models . |
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. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
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| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations (2022.coling-1)
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| Challenge: | Emotion recognition in multi-party conversations is a challenging task that predicts the emotion for each utterance. |
| Approach: | They propose to separate conversations into detached threads to capture emotional clues in conversational context . they propose to use mutual detachment networks to perform context and speaker-specific modeling within detached thread. |
| Outcome: | The proposed model outperforms baseline models on two datasets. |