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.

翻译：基于图神经网络对多方对话进行建模已被证明在捕捉复杂且图结构化的信息流方面具有有效性。然而，现有方法严重依赖必要的收信人标签，且仅能应用于每条语句必须标注收信人标签的理想场景。针对多方对话中普遍存在的收信人标签稀缺问题，我们提出MADNet方法，在异构图神经网络中最大化收信人推断期望以用于多方对话生成。当多方对话存在部分收信人标签缺失时，现有方法无法构建连续的对话图，而只能生成若干孤立的对话片段。为确保这些对话片段间的消息传递，我们设计了四种额外类型的潜在边以构建全连接图。此外，为优化缺乏收信人标签语句的边类型相关消息传递，我们还设计了一种基于期望最大化的方法：通过迭代生成银标准收信人标签（E步）并优化生成回复质量（M步）。在Ubuntu IRC频道两个基准数据集上的实验结果表明，MADNet在多方对话生成任务中优于各类基线模型，尤其在收信人标签部分缺失这一更常见且更具挑战性的场景下表现突出。