  In this paper, we introduce an approach for leveraging available data across
multiple locales sharing the same language to 1) improve domain classification
model accuracy in Spoken Language Understanding and user experience even if new
locales do not have sufficient data and 2) reduce the cost of scaling the
domain classifier to a large number of locales. We propose a locale-agnostic
universal domain classification model based on selective multi-task learning
that learns a joint representation of an utterance over locales with different
sets of domains and allows locales to share knowledge selectively depending on
the domains. The experimental results demonstrate the effectiveness of our
approach on domain classification task in the scenario of multiple locales with
imbalanced data and disparate domain sets. The proposed approach outperforms
other baselines models especially when classifying locale-specific domains and
also low-resourced domains.
