  Generative seq2seq dialogue systems are trained to predict the next word in
dialogues that have already occurred. They can learn from large unlabeled
conversation datasets, build a deeper understanding of conversational context,
and generate a wide variety of responses. This flexibility comes at the cost of
control, a concerning tradeoff in doctor/patient interactions. Inaccuracies,
typos, or undesirable content in the training data will be reproduced by the
model at inference time. We trade a small amount of labeling effort and some
loss of response variety in exchange for quality control. More specifically, a
pretrained language model encodes the conversational context, and we finetune a
classification head to map an encoded conversational context to a response
class, where each class is a noisily labeled group of interchangeable
responses. Experts can update these exemplar responses over time as best
practices change without retraining the classifier or invalidating old training
data. Expert evaluation of 775 unseen doctor/patient conversations shows that
only 12% of the discriminative model's responses are worse than the what the
doctor ended up writing, compared to 18% for the generative model.
