Papers with MPCs
Is ChatGPT a Good Multi-Party Conversation Solver? (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are powerful tools for multi-party conversations, but their capacity to handle multi-parties remains unexplored. |
| Approach: | They propose to evaluate ChatGPT and GPT-4's zero-shot learning capabilities within the context of multi-party conversations (MPCs) they also propose to incorporate MPC structures, encompassing both speaker and addressee architecture. |
| Outcome: | The proposed models perform poorly on a number of MPC tasks while GPT-4 performs well on speaker and addressee architecture. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
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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. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
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. |
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding (2023.acl-long)
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| Challenge: | Existing methods on understanding multi-party conversations typically embed interlocutors and utterances into sequential information flows or use superficial graph structures. |
| Approach: | They propose a plug-and-play method which adapts Transformer-based pre-trained language models for universal MPC understanding. |
| Outcome: | The proposed method can adapt Transformer-based pre-trained language models for universal MPC understanding. |