Multi-Language Hypotheses Ranking And Domain Tracking for Open Domain - Crook, Paul A.; Martin, Jean-Philippe; Sarikaya, Ruhi (Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015), 2015)
Hypothesis ranking (HR) is an approach for improving the accuracy of both domain detection and tracking in multi-domain, multi-turn dialogue systems. This paper presents the results of applying a universal HR model to multiple dialogue systems, each of which are using a different language. It demonstrates that as the set of input features used by HR models are largely language independent a single, universal HR model can be used in place of language specific HR models with only a small loss in accuracy (average absolute gain of +3:55% versus +4:54%), and also such a model can generalise well to new unseen languages, especially related languages (achieving an average absolute gain of +2:8% in domain accuracy on held out locales fr-fr, es-es, it-it; an average of 66% of the gain that could be achieve by training language specific HR models). That the latter is achieved without retraining significantly eases expansion of existing dialogue systems to new locales/languages.