Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption

Hongyin Luo, Shang-Wen Li, James Glass


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.


 DOI: 10.21437/Interspeech.2020-1865

Cite as: Luo, H., Li, S., Glass, J. (2020) Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption. Proc. Interspeech 2020, 3895-3899, DOI: 10.21437/Interspeech.2020-1865.


@inproceedings{Luo2020,
  author={Hongyin Luo and Shang-Wen Li and James Glass},
  title={{Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={3895--3899},
  doi={10.21437/Interspeech.2020-1865},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1865}
}