  Spoken dialog systems have seen applications in many domains, including
medical for automatic conversational diagnosis. State-of-the-art dialog
managers are usually driven by deep reinforcement learning models, such as deep
Q networks (DQNs), which learn by interacting with a simulator to explore the
entire action space since real conversations are limited. However, the
DQN-based automatic diagnosis models do not achieve satisfying performances
when adapted to new, unseen diseases with only a few training samples. In this
work, we propose the Prototypical Q Networks (ProtoQN) as the dialog manager
for the automatic diagnosis systems. The model calculates prototype embeddings
with real conversations between doctors and patients, learning from them and
simulator-augmented dialogs more efficiently. We create both supervised and
few-shot learning tasks with the Muzhi corpus. Experiments showed that the
ProtoQN significantly outperformed the baseline DQN model in both supervised
and few-shot learning scenarios, and achieves state-of-the-art few-shot
learning performances.
