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

A method of operating a natural language understanding (NLU) framework, comprising:

• performing an initial iterative training and tuning of a cross-lingual NLU model until the NLU framework is configured to infer a base language test dataset with at least a first predefined level of prediction performance, wherein the cross-lingual NLU model includes an intent-entity model, a cross-lingual word vector distribution model, and a set of NLU framework parameters, and wherein the intent-entity model defines intents and includes sample utterances for each intent;
• determining a target language test dataset from the base language test dataset;
• selecting a set of target language test utterances from the target language test dataset and adding the selected set of target language test utterances to the intent-entity model as new target language sample utterances;
• and
• performing additional iterative training and tuning of the cross-lingual NLU model until the NLU framework is configured to inference the target language test dataset with at least a second predefined level of prediction performance.

A non-transitory, computer-readable medium storing instructions of a natural language understanding (NLU) framework executable by a computer processor, the instructions comprising instructions to:

• perform an initial iterative training and tuning of a cross-lingual NLU model until the NLU framework is configured to infer a base language test dataset with at least a first predefined level of prediction performance, wherein the cross-lingual NLU model includes an intent-entity model, a cross-lingual word vector distribution model, and a set of NLU framework parameters, and wherein the intent-entity model defines intents and includes sample utterances for each intent;
• determine a target language test dataset from the base language test dataset;
• select a set of target language test utterances from the target language test dataset and adding the selected set of target language test utterances to the intent-entity model as new target language sample utterances;
• and
• perform additional iterative training and tuning of the cross-lingual NLU model until the NLU framework is configured to inference the target language test dataset with at least a second predefined level of prediction performance.