Abstract: Correlations between a set of linguistic features identified in an unstructured text recited in a source language and a set of user engagement analytic parameters may be measured by a machine learning model selected based on a set of performance metrics from a set of machine learning models trained by a set of supervised machine learning algorithms on (i) a set of unstructured texts recited in the source language and containing the set of linguistic features and (ii) the set of user engagement analytic parameters measured for the set of unstructured texts. The machine learning model grades the unstructured text recited in the source language to determine whether the unstructured text recited in the source language should be (1) edited in the source language and then translated into the target language or (2) translated from the source language to the target language as is.

Abstract: Logic to drive workflow for various pivot points at various stages of workflow dispatch, translation, and quality assurance within various modern service delivery and translation management platforms to expedite speed of translation workflow process and improve quality of final translation product, while increasing computational efficiency and decreasing network latency.

Abstract: Logic to drive workflow for various pivot points at various stages of workflow dispatch, translation, and quality assurance within various modern service delivery and translation management platforms to expedite speed of translation workflow process and improve quality of final translation product, while increasing computational efficiency and decreasing network latency.