Prototypical Q Networks for Automatic Conversational Diagnosisand Few-Shot New Disease Adaption
Table 2: Average success rates (%) of the meta-learning tasks with DQN and ProtoQN under different noise levels.
['Noise', '0', '0.1', '0.2', '0.3']
From the table we see that the ProtoQN significantly outperformed the DQN model and achieves SOTA performance under few-shot learning setting. The fact shows that learning shared embeddings from real conversations is more efficient when adapting the model for learning new diseases with few examples. Meanwhile, many shared knowledge from pre-trained diseases can still be applied for the new ones. The learned knowledge about symptoms allows the neural network to learn new diseases with a better initialization of dialog policy, and thus makes the adaptation faster and better. We also found that although the increasing of noise level degrades the performance of DQNs, ProtoQN yields steady success rate when the noise level varies. we believe the robustness results from the ensemble nature of ProtoNet’s inference mechanisms.