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Generative seq2seq dialogue systems are trained to predict the next word indialogues that have already occurred. They can learn from large unlabeledconversation datasets, build a deep understanding of conversational context,and generate a wide variety of responses. This flexibility comes at the cost ofcontrol. Undesirable responses in the training data will be reproduced by themodel at inference time, and longer generations often don't make sense. Insteadof generating responses one word at a time, we train a classifier to choosefrom a predefined list of full responses. The classifier is trained on(conversation context, response class) pairs, where each response class is anoisily labeled group of interchangeable responses. At inference, we generatethe exemplar response associated with the predicted response class. Experts canedit and improve these exemplar responses over time without retraining theclassifier or invalidating old training data. Human evaluation of 775 unseendoctor/patient conversations shows that this tradeoff improves responses. Only12% of our discriminative approach's responses are worse than the doctor'sresponse in the same conversational context, compared to 18% for the generativemodel. A discriminative model trained without any manual labeling of responseclasses achieves equal performance to the generative model.

Classification As Decoder: Trading Flexibility For Control In Neural Dialogue