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. |
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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. |
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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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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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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
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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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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. |
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Bazinga! A Dataset for Multi-Party Dialogues Structuring (2022.lrec-1)
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Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
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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. |
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Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction (2021.findings-acl)
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| Challenge: | Existing methods for identifying relations from dialogues do not fully consider the particularity of dialogues, making them difficult to understand the semantics between conversational arguments. |
| Approach: | They propose two tasks to enhance the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments . |
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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. |