X-SHOT: Learning to Rank Voice Applications Via Cross-Locale Shard-Based Co-Training

Abstract

Virtual assistants such as Google Assistant and Amazon Alexa host thousands of voice applications (skills) that han-dle a very large and diverse array of customer utterances. However, the number of supported skills may be much lower in some locales, particularly in countries other than the United States. Accordingly, customer utterances handled in a popular locale may be going unclaimed in another locale. Moreover, locales with smaller skill ecosystems also suffer from lim-ited labeled data for training systems to route utterances to skills. To tackle these aforementioned challenges, we propose a Cross-locale SHard-based cO-Training model (X-SHOT) that uses an iterative label augmentation approach to retrieve relevant skills in a source locale for unclaimed utterances in a target locale. The obtained results could be further used by skill developers in the source locale to gauge the latent de-mand for their skills in other locales and therefore to prioritize the internationalization of their skills accordingly.