Sdl Inc.

Mihai Vlad

Abdessamad Echihabi

Bryant Huang

Quinn Lam

Bartlomiej Czeslaw Maczynski

Jingyi Han

Ovidiu Petridean

Dragos Stefan Munteanu

Arnaud Simon

Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting machine translated segments of a document and their associated quality estimation scores, invoking an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a quality estimation score of the machine translated segment meets or exceeds a quality estimation score threshold, or a predetermined number of attempts are reached.

Systems and methods of automatic post-editing of machine translated content

A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Systems and methods for intelligent source content routing

Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes a method comprising means for receiving one or more assets that include textual content, means for performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and means for displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Systems and methods of automatic message creation using machine learning on digital assets

Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting a user with machine translated segments of a document and their associated quality estimation scores, allowing a user to invoke an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a satisfactory translation is achieved or a predetermined number of attempts are reached.

Automatic post-editing systems and methods

Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

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London, United Kingdom

Mihai Vlad