The templates in the template ranker are general forms of sentences, with variables for things like product names, dates, delivery times, and prices. But as we test and refine the generative model internally, we plan to begin introducing it into the customer-facing system as well. In the customer-facing system, we’re using the template ranker, which allows us to control the automated agent’s vocabulary. In our internal system, we’re testing both approaches. One uses a neural network to generate responses to customer utterances from scratch, and the other uses a neural network to choose among hand-authored response templates. In that paper, we compare two approaches. We described the basic principles behind both agents in a paper we presented last year at the annual meeting of the North American Chapter of the Association for Computational Linguistics (NAACL). According to that metric, the new agents significantly outperform the old ones.Īt the same time that we’re testing a customer-facing neural agent, we’re also testing a variation on the system that suggests possible responses to our customer service representatives, saving them time. Automation rate combines two factors: whether the automated agent successfully completes a transaction (without referring it to a customer service representative) and whether the customer contacts customer service a second time within 24 hours. In randomized trials, we’ve been comparing the new neural agents to our existing rule-based systems, using a metric called automation rate. These agents can handle a broader range of interactions with better results, allowing our customer service representatives to focus on tasks that depend more on human judgment. On, we’ve started phasing in automated agents that use neural networks rather than rules. If the automated agent can’t handle a request, it refers the request to a human customer service representative. Typically, these agents are governed by rules, rather like flow charts that specify responses to particular customer inputs. Most text-based online customer service systems feature automated agents that can handle simple requests. To help our customer service agents provide support in new regions and with new customers, we’ve begun testing two neural-network-based systems, one that can handle common customer service requests automatically and one that helps customer service agents respond to customers even more easily. One of the ways Amazon continues to work towards its mission of being the “Earth’s most customer-centric company” is through a commitment to world-class customer service.
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