论文标题
用于自动对话诊断和几乎没有新疾病适应的原型Q网络
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption
论文作者
论文摘要
口语对话系统已经在许多领域中看到了应用程序，包括用于自动对话诊断的医疗。最先进的对话框经理通常是由深度强化学习模型（例如Deep Q Networks（DQN））驱动的，这些模型通过与模拟器进行交互来学习，以探索整个动作空间，因为实际对话是有限的。但是，基于DQN的自动诊断模型仅使用少数培训样本适应新的，看不见的疾病，无法实现令人满意的表现。在这项工作中，我们提出了原型Q网络（ProtoQN）作为自动诊断系统的对话管理经理。该模型可以更有效地计算出具有医生和患者之间真实对话的原型嵌入，并在医生和患者之间进行真实对话，并更有效地进行了模拟器。我们使用Muzhi语料库创建了监督和少量学习任务。实验表明，原始QN在监督和少数学习方案中都显着优于基线DQN模型，并实现了最新的少数学习表现。
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.
