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Natural Language Understanding (NLU) is an established component within aconversational AI or digital assistant system, and it is responsible forproducing semantic understanding of a user request. We propose a scalable andautomatic approach for improving NLU in a large-scale conversational AI systemby leveraging implicit user feedback, with an insight that user interactiondata and dialog context have rich information embedded from which usersatisfaction and intention can be inferred. In particular, we propose a generaldomain-agnostic framework for curating new supervision data for improving NLUfrom live production traffic. With an extensive set of experiments, we show theresults of applying the framework and improving NLU for a large-scaleproduction system and show its impact across 10 domains.

A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems