Title | ||
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Multi-Language Hypotheses Ranking And Domain Tracking For Open Domain Dialogue Systems |
Abstract | ||
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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. |
Year | Venue | Keywords |
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2015 | 16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5 | dialogue systems, natural language under-standing, hypothesis ranking, contextual domain classification, multi-language, locale expansion, language independence |
Field | DocType | Citations |
Ranking,Computer science,Speech recognition,Absolute gain,Natural language processing,Artificial intelligence,Multi language,Retraining | Conference | 2 |
PageRank | References | Authors |
0.48 | 8 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Paul A. Crook | 1 | 9 | 2.26 |
Jean-Philippe Robichaud | 2 | 12 | 1.98 |
Ruhi Sarikaya | 3 | 698 | 64.49 |