Multimodal Contextual Dialogue Breakdown Detection for Conversational AI
  Models
Md Messal Monem Miah Ulie Schnaithmann Arushi Raghuvanshi Youngseo Son
Detecting dialogue breakdown in real time is critical for conversational AI
systems, because it enables taking corrective action to successfully complete a
task. In spoken dialog systems, this breakdown can be caused by a variety of
unexpected situations including high levels of background noise, causing STT
mistranscriptions, or unexpected user flows. In particular, industry settings
like healthcare, require high precision and high flexibility to navigate
differently based on the conversation history and dialogue states. This makes
it both more challenging and more critical to accurately detect dialog
breakdown. To accurately detect breakdown, we found it requires processing
audio inputs along with downstream NLP model inferences on transcribed text in
real time. In this paper, we introduce a Multimodal Contextual Dialogue
Breakdown (MultConDB) model. This model significantly outperforms other known
best models by achieving an F1 of 69.27.