Diagnosing and Prioritizing Issues in Automated Order-Taking Systems: A Machine-Assisted Error Discovery Approach

Troubles in speaking, hearing, and understanding occur routinely in any kind of natural conversational setting. The natural flow of conversation includes methods for repairing such troubles by repeating or paraphrasing all or parts of prior turns. In the case of conversational AI systems, these troubles occur due to failure of different components of the system such as the speech recognition, natural language understanding, and natural language generation. Such errors may occur infrequently, but often enough to have a significant impact on key performance indicators (KPIs). Identifying the root cause of these errors is a complex task that requires a team to meticulously examine and interpret the interaction between the voice agent and customers. In this work, we present an interactive system, DTTool, that surfaces system-generated annotations that hint at anomalous events that lead to candidate errors that impact KPIs and demonstrate how the team could discover unknown errors using DTTool.