Infosys has patented a system and method for training a neural machine translation model. The method involves receiving source and target statements, encoding them using a token dictionary, creating a source-target map, generating fragments, and training the model. The innovation aims to improve translation accuracy. GlobalData’s report on Infosys gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Infosys, AI for workflow management was a key innovation area identified from patents. Infosys's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.

Training neural machine translation model using source-target map

Source: United States Patent and Trademark Office (USPTO). Credit: Infosys Ltd

A recently granted patent (Publication Number: US11875141B2) discloses a computer-implemented method for training a neural machine translation model. The method involves receiving source and target statements, encoding them using a predefined token dictionary, creating a source-target map, generating fragments of the statements within a predefined threshold length, and training the model using these fragments. The creation of the source-target map includes identifying tokens, determining intersecting tokens, and generating subsets of the map to ensure accurate training of the model.

Furthermore, the patent also describes a system and a non-transitory computer-readable medium for training the neural machine translation model. The system includes at least one processor and memory unit to execute the method steps, while the computer-readable medium stores instructions for performing the training process. The system and medium follow the same steps as the method, including encoding source and target statements, creating a source-target map, generating fragments iteratively, and training the model. This innovative approach ensures that all tokens in the statements are traversed during the training process, enhancing the accuracy and efficiency of the neural machine translation model.

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