KL regularization aligning model predictions with empirical transition patterns improves macro-F1 by 9-42% in next dialogue act prediction on German counselling data and transfers to other datasets.
Harold Ngabo-Woods, Larisa Dunai, and Isabel Seguí Verdú
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Mental health AI safety evaluations that discard temporal sequence and accumulation produce invalid conclusions; the paper formalizes this as Temporal Safety Non-Identifiability and proposes SCOPE-MH as a reporting standard that preserves evidence.
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Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations
KL regularization aligning model predictions with empirical transition patterns improves macro-F1 by 9-42% in next dialogue act prediction on German counselling data and transfers to other datasets.
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Mental Health AI Safety Claims Must Preserve Temporal Evidence
Mental health AI safety evaluations that discard temporal sequence and accumulation produce invalid conclusions; the paper formalizes this as Temporal Safety Non-Identifiability and proposes SCOPE-MH as a reporting standard that preserves evidence.