  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 deep understanding of conversational context,
and generate a wide variety of responses. This flexibility comes at the cost of
control. Undesirable responses in the training data will be reproduced by the
model at inference time, and longer generations often don't make sense. Instead
of generating responses one word at a time, we train a classifier to choose
from a predefined list of full responses. The classifier is trained on
(conversation context, response class) pairs, where each response class is a
noisily labeled group of interchangeable responses. At inference, we generate
the exemplar response associated with the predicted response class. Experts can
edit and improve these exemplar responses over time without retraining the
classifier or invalidating old training data. Human evaluation of 775 unseen
doctor/patient conversations shows that this tradeoff improves responses. Only
12% of our discriminative approach's responses are worse than the doctor's
response in the same conversational context, compared to 18% for the generative
model. A discriminative model trained without any manual labeling of response
classes achieves equal performance to the generative model.
