RWS has been granted a patent for automatic post-editing of machine translated content. The method involves presenting machine translated segments with quality scores, using a generative AI model to produce revised translations, and iterating until satisfactory results are achieved. GlobalData’s report on RWS gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on RWS, was a key innovation area identified from patents. RWS's grant share as of May 2024 was 54%. Grant share is based on the ratio of number of grants to total number of patents.
Automated post-editing of machine translated content

A recently granted patent (Publication Number: US11995414B1) outlines a method for automated post-editing of machine-translated content. The method involves presenting users with machine-translated segments of a document along with quality estimation scores, allowing users to invoke an automated post-editing system for segments with unsatisfactory scores. The unsatisfactory segments are inputted into a generative AI model that uses contextual information to produce a revised translation through iterative processes. The generative AI model, based on a transformer architecture, aims to enhance the quality of translations by processing unsatisfactory aspects of previous attempts.
Furthermore, the patent describes a system for automated improvement of machine-translated content, comprising a machine translation engine, a quality estimation model, and a generative AI model. The system provides an interface for users to view machine-translated segments, quality scores, and options for invoking automatic post-editing. The generative AI model utilizes contextual information and feedback from the quality estimation model to iteratively generate improved translations. The system also allows users to manually intervene and provides a feedback mechanism for enhancing the output based on previous iterations and prompts. Overall, the patent focuses on enhancing the quality of machine-translated content through automated post-editing processes and iterative improvements based on user feedback and contextual information.
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