  Existing neural models for dialogue response generation assume that
utterances are sequentially organized. However, many real-world dialogues
involve multiple interlocutors (i.e., multi-party dialogues), where the
assumption does not hold as utterances from different interlocutors can occur
"in parallel." This paper generalizes existing sequence-based models to a
Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is
a graph-based encoder that can model the information flow along the
graph-structured dialogues (two-party sequential dialogues are a special case).
Experimental results show that GSN significantly outperforms existing
sequence-based models.
