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View all- Kuligowska KStanusch M(2024)Commercial chatbot monitoring: Approaches focused on automated conversation analysisHumanities & Social Sciences Reviews10.18510/hssr.2024.122712:2(54-60)Online publication date: 6-Sep-2024
Machine-Assisted Error Discovery in Conversational AI Systems
Authors: 
Maeda F Hanafi, Frederick Reiss, Yannis Katsis, Robert Moore, David Wood, Mohammad H Falakmasir, Changchang LiuAuthors Info & Claims
Article No.: 234, Pages 1 - 10
Abstract
Troubles in speaking, hearing, and understanding occur routinely in any kind of 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 still 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.
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May 2024
4761 pages
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DOI:10.1145/3613905
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View all- Kuligowska KStanusch M(2024)Commercial chatbot monitoring: Approaches focused on automated conversation analysisHumanities & Social Sciences Reviews10.18510/hssr.2024.122712:2(54-60)Online publication date: 6-Sep-2024
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