Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?
원문 초록
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors.
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Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling
Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot
A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
Confidence Estimation for LLM-Based Dialogue State Tracking
Continual Dialogue State Tracking via Reason-of-Select Distillation
TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation
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