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    <abstract>Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an “@” or other equivalent addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.</abstract>
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%0 Conference Proceedings
%T MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
%A Gu, Jia-Chen
%A Tan, Chao-Hong
%A Chu, Caiyuan
%A Ling, Zhen-Hua
%A Tao, Chongyang
%A Liu, Quan
%A Liu, Cong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gu-etal-2023-madnet
%X Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an “@” or other equivalent addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.
%R 10.18653/v1/2023.emnlp-main.476
%U https://aclanthology.org/2023.emnlp-main.476/
%U https://doi.org/10.18653/v1/2023.emnlp-main.476
%P 7681-7692

Markdown (Informal)

[MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation](https://aclanthology.org/2023.emnlp-main.476/) (Gu et al., EMNLP 2023)

• MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (Gu et al., EMNLP 2023)

ACL

• Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling, Chongyang Tao, Quan Liu, and Cong Liu. 2023. MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7681–7692, Singapore. Association for Computational Linguistics.