Abstract: Systems and methods for generating a natural language domain corpus to train a machine learning natural language understanding process. A base utterance expressing an intent and an intent profile indicating at least one of categories, keywords, concepts, sentiment, entities, or emotion of the intent are received. Machine translation translates the base utterance into a plurality of foreign language utterances and back into respective utterances in the target natural language to create a normalized utterance set. Analysis of each utterance in the normalized utterance set determines respective meta information for each such utterance. Comparison of the meta information to the intent profile determines a highest ranking matching utterance within the normalized utterance set. A set of natural language data to train a machine learning natural language understating process is created based on further natural language translations of the highest ranking matching utterance.

Abstract: Systems and methods for generating a natural language domain corpus to train a machine learning natural language understanding process. A base utterance expressing an intent and an intent profile indicating at least one of categories, keywords, concepts, sentiment, entities, or emotion of the intent are received. Machine translation translates the base utterance into a plurality of foreign language utterances and back into respective utterances in the target natural language to create a normalized utterance set. Analysis of each utterance in the normalized utterance set determines respective meta information for each such utterance. Comparison of the meta information to the intent profile determines a highest ranking matching utterance within the normalized utterance set. A set of natural language data to train a machine learning natural language understating process is created based on further natural language translations of the highest ranking matching utterance.