  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.
