In a narrow domain, such as customer service, there are common recurring questions and answers. To take advantage of this repetition, you can train a NN to find appropriate question-answer pairs. Amazon presents a new method of training a simple dual-encoder to identify question-answer pairs based on the embeddings of past responses. The trained encoder then selects the appropriate response template. In their experiments, they found that the selected templates cover >70% of past customer inquiries.
Amazon’s New Dual-Encoder for Question-Answer Pair Selection
Post · Mar 29, 2017 17:13 · Share on Twitter
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.