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.

本文提出了一种采用双编码器网络进行弱监督训练的原型对话响应生成模型，从过去的代理回答中提取嵌入导出的答案模板，选择最佳模板生成对客户咨询的回答。在像客户服务这样的封闭领域中，所选模板可以涵盖过去咨询的 70％以上。此外，模型选择的模板的相关性显著优于标准 tf-idf 基线选择的模板。