  While rich, open-domain textual data are generally available and may include
interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for
language processing tasks, and are usually in a non-conversational format. In
this work, we take a step towards automatically generating conversational data
using Generative Conversational Networks, aiming to benefit from the breadth of
available language and knowledge data, and train open domain social
conversational agents. We evaluate our approach on conversations with and
without knowledge on the Topical Chat dataset using automatic metrics and human
evaluators. Our results show that for conversations without knowledge
grounding, GCN can generalize from the seed data, producing novel conversations
that are less relevant but more engaging and for knowledge-grounded
conversations, it can produce more knowledge-focused, fluent, and engaging
conversations. Specifically, we show that for open-domain conversations with
10\% of seed data, our approach performs close to the baseline that uses 100%
of the data, while for knowledge-grounded conversations, it achieves the same
using only 1% of the data, on human ratings of engagingness, fluency, and
relevance.
