Cross-lingual natural language understanding model for multi-language natural language understanding (mNLU)
AssigneeServiceNow, Inc.
InventorsMayank Jain, Murali B. Subbarao
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Abstract
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.
Claims20
A natural language understanding (NLU) framework, comprising:
- at least one memory configured to store a cross-lingual NLU model and a base language test dataset, wherein the cross-lingual NLU model includes an intent-entity model and a cross-lingual word vector distribution model, wherein the intent-entity model defines intents and includes sample utterances for each intent;
- and
- at least one processor configured to execute stored instructions to cause the NLU framework to perform actions comprising:
- performing an initial iterative training and tuning of the cross-lingual NLU model until the NLU framework is configured to infer the base language test dataset with at least a first predefined level of prediction performance;
- 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 infer the target language test dataset with at least a second predefined level of prediction performance.
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 infer 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 infer the target language test dataset with at least a second predefined level of prediction performance.
Dependencies are inferred from the claim text.