Data-driven automated selection of profiles of translation professionals for translation tasks

Abstract

The subject matter of this specification can be implemented in, among other things, a method that includes storing previous translations of electronic documents for profiles of translation professionals. The method includes receiving a request to translate an electronic document. The method includes selecting ones of the profiles as being experienced in at least one subject area of the electronic document based on a proximity of terms or subject areas in the electronic documents translated by the ones of the profiles to terms or the subject area of the electronic document. The method includes evaluating qualities of the previous translations for each of the selected ones of the profiles. The method includes planning a workflow for translation of the electronic document based on the selected ones of the profiles and the qualities of the previous translations. The method includes causing the electronic document to be translated according to the planned workflow.

What is claimed is: 
     
         1 . A method comprising:
 storing, in a data storage device, a plurality of previous translations of prior electronic documents for a plurality of profiles of translation professionals;   extracting, by at least one processor, a plurality of terms comprising words and patterns of words from the prior electronic documents;   receiving, from a client system, a request to translate a current electronic document from a source language to a target language;   selecting, by the processor, one or more of the plurality of profiles based on a proximity of the plurality of terms extracted from text in the prior electronic documents translated by the one or more of the profiles to extracted terms of the current electronic document;   evaluating, by the processor, qualities of the previous translations of the prior electronic documents for each of the selected one or more of the profiles;   planning, by the processor, a workflow for translation of the current electronic document based on the selected one or more of the profiles and the qualities of the previous translations; and   causing the current electronic document to be translated according to the planned workflow.   
     
     
         2 . The method of  claim 1 , further comprising:
 determining, by the processor, a predicted translation time or a predicted translation accuracy of the current electronic document using the qualities of the previous translations for each of the selected one or more of the profiles.   
     
     
         3 . The method of  claim 2 , further comprising:
 using the current electronic document and an input profile of the selected one or more of the profiles as input to a machine learning (ML) model; and   obtaining one or more outputs of the ML model, the one or more outputs of the ML model indicating the predicted translation time or the predicted translation accuracy of the current electronic document by the input profile.   
     
     
         4 . The method of  claim 3 , wherein the ML model is trained using training data with training inputs comprising the plurality of previous translations and target outputs for respective training inputs, the target outputs comprising a plurality of corrections associated with the plurality of previous translations. 
     
     
         5 . The method of  claim 1 , further comprising:
 training a first machine learning (ML) model to evaluate a correlation between automatically measured parameters and reviewer corrections of the previous translations such that the first trained ML model receives the electronic document as input and outputs data indicating a predicted number of corrections for each of the selected one or more of the profiles to translate the current electronic document;   training a second ML model to evaluate a correlation between the reviewer corrections and human quality evaluations of the previous translations such that the second trained ML model receives the predicted number of corrections as input and outputs data indicating a predicted quality evaluation for each of the selected one or more of the profiles to translate the current electronic document; and   training a third ML model to evaluate a correlation between the automatically measured parameters and the human quality evaluation of the previous translations such that the third trained ML model receives the predicted quality evaluation as input and outputs data indicating a final evaluation and quality projection for each of the selected one or more of the profiles to translate the current electronic document.   
     
     
         6 . The method of  claim 5 , wherein the automatically measured parameters comprises at least one of:
 time spent translating segments of the plurality of previous translations by the selected one or more of the profiles;   a number of actions taken by the selected one or more of the profiles to translate the segments of the plurality of the previous translations; or   a type of correction incurred by the selected one or more of the profiles while translating the plurality of previous translations.   
     
     
         7 . The method of  claim 1 , wherein the qualities of the previous translations comprise one or more scores of translation tests associated with the selected one or more of the profiles, wherein the translation tests and the current electronic document both comprise the same subject. 
     
     
         8 . A system comprising:
 a memory; and   a processing device, communicatively coupled to the memory, to:
 store, in a data storage device, a plurality of previous translations of prior electronic documents for a plurality of profiles of translation professionals; 
 extract a plurality of terms comprising words and patterns of words from the prior electronic documents; 
 receive, from a client system, a request to translate a current electronic document from a source language to a target language; 
 select one or more of the plurality of profiles based on a proximity of the plurality of terms extracted from text in the prior electronic documents translated by the one or more of the profiles to extracted terms of the current electronic document; 
 evaluate qualities of the previous translations of the prior electronic documents for each of the selected one or more of the profiles; 
 plan a workflow for translation of the current electronic document based on the selected one or more of the profiles and the qualities of the previous translations; and 
 cause the current electronic document to be translated according to the planned workflow. 
   
     
     
         9 . The system of  claim 8 , wherein the processing device is further to:
 determine a predicted translation time or a predicted translation accuracy of the current electronic document using the qualities of the previous translations for each of the selected one or more of the profiles.   
     
     
         10 . The system of  claim 9 , wherein the processing device is further to:
 use the current electronic document and an input profile of the selected one or more of the profiles as input to a machine learning (ML) model; and   obtain one or more outputs of the ML model, the one or more outputs of the ML model indicating the predicted translation time or the predicted translation accuracy of the current electronic document by the input profile.   
     
     
         11 . The system of  claim 10 , wherein the ML model is trained using training data with training inputs comprising the plurality of previous translations and target outputs for respective inputs, the target outputs comprising a plurality of corrections associated with the plurality of previous translations. 
     
     
         12 . The system of  claim 8 , wherein the processing device is further to:
 train a first machine learning (ML) model to evaluate a correlation between automatically measured parameters and reviewer corrections of the previous translations such that the first trained ML model receives the electronic document as input and outputs data indicating a predicted number of corrections for each of the selected one or more of the profiles to translate the current electronic document;   train a second ML model to evaluate a correlation between the reviewer corrections and human quality evaluations of the previous translations such that the second trained ML model receives the predicted number of corrections as input and outputs data indicating a predicted quality evaluation for each of the selected one or more of the profiles to translate the current electronic document; and   train a third ML model to evaluate a correlation between the automatically measured parameters and the human quality evaluation of the previous translations such that the third trained ML model receives the predicted quality evaluation as input and outputs data indicating a final evaluation and quality projection for each of the selected one or more of the profiles to translate the current electronic document.   
     
     
         13 . The system of  claim 12 , wherein the automatically measured parameters comprises at least one of:
 time spent translating segments of the plurality of previous translations by the selected one or more of the profiles;   a number of actions taken by the selected one or more of the profiles to translate the segments of the plurality of the previous translations; or   a type of correction incurred by the selected one or more of the profiles while translating the plurality of previous translations.   
     
     
         14 . The system of  claim 8 , wherein the qualities of the previous translations comprise one or more scores of translation tests associated with the selected one or more of the profiles, wherein the translation tests and the current electronic document both comprise the same subject. 
     
     
         15 . A non-transitory machine-readable storage medium comprising instructions that, when executed by a processing device, cause the processing device to:
 store, in a data storage device, a plurality of previous translations of prior electronic documents for a plurality of profiles of translation professionals;   extract a plurality of terms comprising words and patterns of words from the prior electronic documents;   receive, from a client system, a request to translate a current electronic document from a source language to a target language;   select one or more of the plurality of profiles based on a proximity of the plurality of terms extracted from text in the prior electronic documents translated by the one or more of the profiles to extracted terms of the current electronic document;   evaluate qualities of the previous translations of the prior electronic documents for each of the selected one or more of the profiles;   plan a workflow for translation of the current electronic document based on the selected one or more of the profiles and the qualities of the previous translations; and   cause the current electronic document to be translated according to the planned workflow.   
     
     
         16 . The non-transitory machine-readable storage medium of  claim 15 , wherein the processing device is further to:
 determine a predicted translation time or a predicted translation accuracy of the current electronic document using the qualities of the previous translations for each of the selected one or more of the profiles.   
     
     
         17 . The non-transitory machine-readable storage medium of  claim 16 , wherein the processing device is further to:
 use the current electronic document and an input profile of the selected one or more of the profiles as input to a machine learning (ML) model; and   obtain one or more outputs of the ML model, the one or more outputs of the ML model indicating the predicted translation time or the predicted translation accuracy of the current electronic document by the input profile.   
     
     
         18 . The non-transitory machine-readable storage medium of  claim 17 , wherein the ML model is trained using training data with training inputs comprising the plurality of previous translations and target outputs for respective training inputs, the target outputs comprising a plurality of corrections associated with the plurality of previous translations. 
     
     
         19 . The non-transitory machine-readable storage medium of  claim 15 , wherein the processing device is further to:
 train a first machine learning (ML) model to evaluate a correlation between automatically measured parameters and reviewer corrections of the previous translations such that the first trained ML model receives the electronic document as input and outputs data indicating a predicted number of corrections for each of the selected one or more of the profiles to translate the current electronic document;   train a second ML model to evaluate a correlation between the reviewer corrections and human quality evaluations of the previous translations such that the second trained ML model receives the predicted number of corrections as input and outputs data indicating a predicted quality evaluation for each of the selected one or more of the profiles to translate the current electronic document; and   train a third ML model to evaluate a correlation between the automatically measured parameters and the human quality evaluation of the previous translations such that the third trained ML model receives the predicted quality evaluation as input and outputs data indicating a final evaluation and quality projection for each of the selected one or more of the profiles to translate the current electronic document.   
     
     
         20 . The non-transitory machine-readable storage medium of  claim 19 , wherein the automatically measured parameters comprises at least one of:
 time spent translating segments of the plurality of previous translations by the selected one or more of the profiles;   a number of actions taken by the selected one or more of the profiles to translate the segments of the plurality of the previous translations; or   a type of correction incurred by the selected one or more of the profiles while translating the plurality of previous translations.