  We describe a prototype dialogue response generation model for the customer
service domain at Amazon. The model, which is trained in a weakly supervised
fashion, measures the similarity between customer questions and agent answers
using a dual encoder network, a Siamese-like neural network architecture.
Answer templates are extracted from embeddings derived from past agent answers,
without turn-by-turn annotations. Responses to customer inquiries are generated
by selecting the best template from the final set of templates. We show that,
in a closed domain like customer service, the selected templates cover $>$70\%
of past customer inquiries. Furthermore, the relevance of the model-selected
templates is significantly higher than templates selected by a standard tf-idf
baseline.
