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Hypotheses Ranking for Robust Domain Classification And Tracking in Dialogue Systems

Jean-Philippe Robichaud, Paul A. Crook, Puyang Xu, Omar Zia Khan, and Ruhi Sarikaya

Abstract

We present a novel application of hypothesis ranking (HR) for the task of domain detection in a multi-domain, multiturn dialog system. Alternate, domain dependent, semantic frames from a spoken language understanding (SLU) analysis are ranked using a gradient boosted decision trees (GBDT) ranker to determine the most likely domain. The ranker, trained using Lambda Rank, makes use of a range of signals derived from the SLU and previous turn context to improve domain detection. On a multi-turn corpus we show that this approach offers accuracy improvements of 3.2% absolute (25.6% relative) compared to relying solely on upfront non-contextual SLU domain models and 2.9% (24.5% relative) improvement even with contextual SLU domain models. We also show that HR can be trained to be robust to changes in the SLU.

Details

Publication typeInproceedings
Published inProceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014)
PublisherISCA - International Speech Communication Association
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