Oracle international corporation (20240127008). MULTI-LINGUAL NATURAL LANGUAGE GENERATION simplified abstract

MULTI-LINGUAL NATURAL LANGUAGE GENERATION

Organization Name

oracle international corporation

Inventor(s)

Praneet Pabolu of Bangalore (IN)

Karan Dua of Najibabad (IN)

Sriram Chaudhury of Bangalore (IN)

MULTI-LINGUAL NATURAL LANGUAGE GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240127008 titled 'MULTI-LINGUAL NATURAL LANGUAGE GENERATION

Simplified Explanation

The method described in the abstract involves training a base model for text generation and summarization using multiple languages and datasets. Here is a simplified explanation of the abstract:

• The method involves preparing a base model using an input model pretrained on at least three different languages.
• The base vocabulary includes words from two languages among the three languages used for training the input model.
• The base model is constrained to the words in the base vocabulary.
• The base model is trained using enhanced training datasets generated from public data to create text summarization and generation models.
• The text generation model is further enhanced by training it with a second enhanced training dataset generated from the first enhanced dataset.
• Finally, a next sentence generation model is created by training the base model with a third enhanced training dataset generated using the text summarization model and the second enhanced training dataset.

Potential Applications

This technology can be applied in various fields such as natural language processing, machine translation, and content generation.

Problems Solved

This method addresses the challenge of training models for text generation and summarization using multiple languages and datasets effectively.

Benefits

The benefits of this technology include improved text generation and summarization capabilities, enhanced multilingual support, and better performance in handling diverse datasets.

Potential Commercial Applications

This technology can be utilized in industries such as content creation, language translation services, and automated summarization tools.

Possible Prior Art

One possible prior art for this technology could be the use of multilingual training datasets in natural language processing models to improve performance and accuracy.

How does this method compare to existing approaches in multilingual text generation and summarization?

This article does not provide a direct comparison with existing methods in the field. Further research or a comparative study would be needed to evaluate the effectiveness of this approach against other techniques.

What are the potential limitations or challenges of implementing this method in real-world applications?

The article does not address potential limitations or challenges that may arise when implementing this method in practical settings. Additional information on scalability, computational resources, or data requirements would be necessary to assess the feasibility of deploying this technology.

Original Abstract Submitted

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.