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Digital assistants are experiencing rapid growth due to their ability toassist users with day-to-day tasks where most dialogues are happeningmulti-turn. However, evaluating multi-turn dialogues remains challenging,especially at scale. We suggest a context-sensitive method to estimate theturn-level satisfaction for dialogue considering various types of userpreferences. The costs of interactions between users and dialogue systems areformulated using a budget consumption concept. We assume users have an initialinteraction budget for a dialogue formed based on the task complexity and thateach turn has a cost. When the task is completed, or the budget has beenexhausted, users quit the dialogue. We demonstrate our method's effectivenessby extensive experimentation with a simulated dialogue platform and realmulti-turn dialogues.

DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues