MADNet：最大化听话人推断期望以用于多方对话生成
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
AI总结：
针对多方对话中听话人标签稀缺问题，提出MADNet模型，通过设计潜在边完成全连接图并采用期望最大化方法迭代生成银标准标签，有效提升了多方对话生成的性能。
摘要：
使用图神经网络对多方对话（MPCs）进行建模已被证明能有效捕捉复杂的图状信息流。然而，现有方法严重依赖必要的听话人标签，且只能应用于每个话语都必须标有听话人标签的理想场景。为了研究MPCs中听话人标签稀缺这一常见问题，我们提出了MADNet，它在用于MPC生成的异构图神经网络中最大化听话人推断期望。给定一个缺失部分听话人标签的MPC，现有方法无法构建连续连接的对话图，而只能构建出少数几个分离的对话片段。为了确保这些对话片段之间的消息传递，设计了四种额外的潜在边类型来完成一个全连接图。此外，为了针对没有听话人标签的话语优化依赖边类型的消息传递，设计了一种基于期望最大化的方法，该方法迭代地生成银标准听话人标签（E步），并优化生成回复的质量（M步）。在两个Ubuntu IRC频道基准上的实验结果表明，MADNet在MPC生成任务上优于各种基线模型，尤其是在缺失部分听话人标签这一更常见且更具挑战性的场景下。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
