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

arXiv: https://arxiv.org/abs/2005.11153 · HTML: https://mlsanity.com/paper/2005.11153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 3 of 3)

- [Diaformer: Automatic Diagnosis via Symptoms Sequence Generation](https://mlsanity.com/paper/2112.10433/) (2021)
- [Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks](https://mlsanity.com/paper/2101.09773/) (2021)
- [Meta-learning for downstream aware and agnostic pretraining](https://mlsanity.com/paper/2106.03270/) (2021)

## Abstract (arXiv)

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.