论文标题
从隐性用户反馈中学习的可扩展框架，以提高大规模对话AI系统中的自然语言理解
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
论文作者
论文摘要
自然语言理解（NLU）是对话式AI或数字助手系统中的建立组件，并且负责对用户请求产生语义理解。我们提出了一种可扩展的自动方法，用于通过利用隐式用户反馈来改善大规模的对话AI系统中的NLU，并深入了解用户交互数据和对话框上下文具有丰富的信息，可以从中推断出用户满意度和意图。特别是，我们提出了一个通用领域无知的框架，用于策划新的监督数据，以改善NLU从实时生产流量中。通过广泛的实验，我们显示了应用框架并改善NLU的大规模生产系统的结果，并在10个领域显示其影响。
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. In particular, we propose a general domain-agnostic framework for curating new supervision data for improving NLU from live production traffic. With an extensive set of experiments, we show the results of applying the framework and improving NLU for a large-scale production system and show its impact across 10 domains.
