Learning to Balance Grounding Rationales for Dialogue Systems
Joshua A. Gordon, Rebecca J. Passonneau, Susan L. Epstein · 2011
This paper reports on an experiment that investigates clarification subdialogues in intentionally noisy speech recognition. The architecture learns weights for mixtures of grounding strategies from examples provided by a human wizard embedded in the system. Results indicate that the architecture learns to eliminate misunderstandings reliably despite high word error rate. 1