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.

本文提出了一种利用具有相同语言的多个地方可用数据的方法，以提高口语语言理解中的域分类模型准确性和用户体验，即使新的地方没有足够的数据，并减少将域分类器扩展到大量场所的成本。我们建议了一种基于选择性多任务学习的locale-agnostic通用域分类模型，该模型学习具有不同域集的地点上的话语的联合表示，并允许地点根据域的不同选择性地共享知识。实验结果表明，我们的方法在具有不平衡数据和不同域集的多个场所的域分类任务上是有效的。所提出的方法特别优于其他基线模型，尤其是在分类特定于本地区的领域和低资源领域时。

口语理解中的区域不可知通用领域分类模型