Oracle International Corporation

Karan Dua

Sriram Chaudhury

Praneet Pabolu

Mengqing Guo

A method includes preparing a base model using an input model pretrained on at least three languages different from each other and a base vocabulary including words corresponding to two languages among the at least three languages, where the preparing the base model includes constraining the input model to the words included in the base vocabulary; training the base model using a first enhanced training dataset generated from public data, to generate a text summarization model; training the base model using a second enhanced training dataset generated from the first enhanced training dataset, to generate a text generation model; and training the base model using a third enhanced training dataset that is generated using the second enhanced training dataset and the text summarization model, to generate a next sentence generation model.

Multi-lingual natural language generation

The system generates templates to be used for use in synthetic document generation. Generating a template includes selecting entities to be included in the template and/or selecting characteristics of entities in the template. The system may execute randomization functions to determine which entities, of a candidate set of entities, are to be included in a template. The randomization functions may accept, as input, probabilities associated with the entities to compute the inclusion or exclusion of entities in the template. Different entities may be associated with different corresponding probabilities. The system may execute randomization functions to determine characteristics of entities that are included in a template. The characteristics may be selected from a specific candidate set of characteristics or a range of characteristic values.

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Najibabad, India

Karan Dua