  Inspired by recent work in meta-learning and generative teaching networks, we
propose a framework called Generative Conversational Networks, in which
conversational agents learn to generate their own labelled training data (given
some seed data) and then train themselves from that data to perform a given
task. We use reinforcement learning to optimize the data generation process
where the reward signal is the agent's performance on the task. The task can be
any language-related task, from intent detection to full task-oriented
conversations. In this work, we show that our approach is able to generalise
from seed data and performs well in limited data and limited computation
settings, with significant gains for intent detection and slot tagging across
multiple datasets: ATIS, TOD, SNIPS, and Restaurants8k. We show an average
improvement of 35% in intent detection and 21% in slot tagging over a baseline
model trained from the seed data. We also conduct an analysis of the novelty of
the generated data and provide generated examples for intent detection, slot
tagging, and non-goal oriented conversations.
