MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)
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| Challenge: | Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing. |
| Approach: | They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. |
| Outcome: | The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing. |
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| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
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EM Pre-training for Multi-party Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to pretrain large language models for dialogue response generation are difficult due to the lack of annotated addressee labels in multi-party dialogue datasets. |
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| Challenge: | Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. |
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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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Persona-aware Multi-party Conversation Response Generation (2024.lrec-main)
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| Challenge: | Multiparty social dialogue is difficult to formalize and expensive to evaluate, especially at scale. |
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| Challenge: | Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. |
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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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