A practical approach to dialogue response generation in closed domains

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 architect...

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Bibliographic Details
Published in:arXiv.org 2017-03
Main Authors: Lu, Yichao, Keung, Phillip, Zhang, Shaonan, Sun, Jason, Bhardwaj, Vikas
Format: Article
Language:English
Subjects:
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Customer services
Domains
Electronic commerce
Neural networks
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Summary: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.
ISSN:2331-8422