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.

提出了一种预期最大增价网络（MADNet），用于建模具有缺失代表标签的多方对话，实现全连接图的生成并优化了生成应答的质量。

MADNet：基于最大化受话方推断期望的多方会话生成

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party  Conversation Generation

Dialogue response generation requires an agent to generate a response
according to the current dialogue history, in terms of which two-party
dialogues have been well studied, but leaving a great gap for multi-party
dialogues at the same time. Different from two-party dialogues where each
response is a direct reply to its previous utterance, the addressee of a
response utterance should be specified before it is generated in the
multi-party scenario. Thanks to the huge amount of two-party conversational
data, various pre-trained language models for two-party dialogue response
generation have been proposed. However, due to the lack of annotated addressee
labels in multi-party dialogue datasets, it is hard to use them to pre-train a
response generation model for multi-party dialogues. To tackle this obstacle,
we propose an Expectation-Maximization (EM) approach that iteratively performs
the expectation steps to generate addressee labels, and the maximization steps
to optimize a response generation model. Theoretical analyses and extensive
experiments have justified the feasibility and effectiveness of our proposed
method.

此篇论文提出了一种基于 EM（期望最大化）算法的方法，用于优化多方对话应答生成模型，解决多方对话数据中缺乏标注指定对话对象的标签的问题。