CROSS-LINGUAL NATURAL LANGUAGE UNDERSTANDING MODEL FOR MULTI-LANGUAGE NATURAL LANGUAGE UNDERSTANDING (mNLU)

A cross-lingual Natural Language Understanding (NLU) framework includes a cross-lingual NLU model that can be trained and tuned on a base language and one or more target languages, and subsequently be used to infer utterances in any of the supported languages. The present technique enables minimal, targeted fine-tuning of the cross-lingual NLU model in each language to be supported without negatively impacting prediction performance in other languages. Accordingly, the present technique reduces the resource costs in developing and maintaining a multi-language NLU (mNLU) framework and improves the scalability of the mNLU framework to support different languages.