  Existing neural response generation models have achieved impressive
improvements for two-party conversations, which assume that utterances are
sequentially organized. However, many real-world dialogues involve multiple
interlocutors and the structure of conversational context is much more complex,
e.g. utterances from different interlocutors can occur "in parallel". Facing
this challenge, there are works trying to model the relations among utterances
or interlocutors to facilitate response generation with clearer context.
Nonetheless, these methods rely heavily on such relations and all assume that
these are given beforehand, which is impractical and hinders the generality of
such methods. In this work, we propose to automatically infer the relations via
relational thinking on subtle clues inside the conversation context without any
human label, and leverage these relations to guide the neural response
generation. Specifically, we first apply a deep graph random process to fully
consider all possible relations among utterances in the conversational context.
Then the inferred relation graphs are integrated with a variational
auto-encoder framework to train a GAN for structure-aware response generation.
Experimental results on the Ubuntu Internet Relay Chat (IRC) channel benchmark
and the most recent Movie Dialogues show that our method outperforms various
baseline models for multi-party response generation.
