Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
| Challenge: | a dataset of 16 TV and movie series is filled with challenging multi-party dialogues. |
| Approach: | They propose a dataset built around 16 TV and movie series with challenging multi-party dialogues. |
| Outcome: | The proposed dataset is a step towards better multi-party dialogue structuring and understanding. |
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Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
| Challenge: | Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue. |
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| Outcome: | The proposed corpus spans four genres of multi-party conversations from different modalities. |
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. |
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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. |
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Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models (L18-1)
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| Challenge: | Existing methods for speaker modeling are based on hand-crafted statistics and ad hoc to a certain application. |
| Approach: | They propose to use speaker classification as a surrogate task for general speaker modeling and collect massive data to facilitate research in this direction. |
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The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
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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. |
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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. |
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Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)
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| Challenge: | Existing datasets for entity coreference resolution are limited to English and other languages are rare. |
| Approach: | They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi. |
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Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)
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Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang
| Challenge: | Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data. |
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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. |
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