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.
@article{arxiv.1703.09439,
title = {A practical approach to dialogue response generation in closed domains},
author = {Yichao Lu and Phillip Keung and Shaonan Zhang and Jason Sun and Vikas Bhardwaj},
journal= {arXiv preprint arXiv:1703.09439},
year = {2017}
}