Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
This addresses the cost and latency issues for users of crowd-powered conversational assistants, representing an incremental improvement by integrating automation into an existing approach.
The paper tackles the problem of high latency and cost in crowd-powered conversational assistants by introducing Evorus, which automates itself over time through chatbot integration, answer reuse, and automatic approval, achieving maintained conversation quality in a 5-month deployment with 80 participants and 281 conversations.
Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.