Proposed are a multilingual subtitle service system and a method for controlling a service server thereof. The subtitle service system includes: a subtitle service server configured to, in response to a request from a worker, provide a subtitle content creating tool for creating a subtitle content for a content image requested by a client and evaluate task performance of the worker based on the subtitle content created by the worker; and a user terminal device configured to access the subtitle service server to transmit project information on the content image requested by the client, and, in response to a request from the worker, display a subtitle service window including the subtitle content creating tool provided by the subtitle service server.

Embodiments of the present disclosure relate to a solution for translation verification and correction. According to the solution, a neural network is trained to determine an association degree among a group of words in a source or target language. The neural network can be used for translation verification and correction. According to the solution, a group of words in a source language and translations of the group of words in a target language are obtained. An association degree among the group of words and an association degree among the translations can be determined by using the trained neural network. Then, whether there is a wrong translation can be determined based on the association degrees. In some embodiments, corresponding methods, systems and computer program products are provided.

Embodiments of the present disclosure relate to a solution for translation verification and correction. According to the solution, a neural network is trained to determine an association degree among a group of words in a source or target language. The neural network can be used for translation verification and correction. According to the solution, a group of words in a source language and translations of the group of words in a target language are obtained. An association degree among the group of words and an association degree among the translations can be determined by using the trained neural network. Then, whether there is a wrong translation can be determined based on the association degrees. In some embodiments, corresponding methods, systems and computer program products are provided.

The accuracy of machine translation is increased. A translated document with high translation accuracy is obtained. An original document is faithfully translated. An original document is translated with a neural network to generate a first translated document; a modification-target word or phrase is determined from words and phrases contained in the original document on the basis of an analysis result for the first translated document; the modification-target word or phrase is replaced with a high frequency word in learning data used for learning in the neural network to modify the original document; and the modified original document is translated with the neural network to generate a second translated document.

The accuracy of machine translation is increased. A translated document with high translation accuracy is obtained. An original document is faithfully translated. An original document is translated with a neural network to generate a first translated document; a modification-target word or phrase is determined from words and phrases contained in the original document on the basis of an analysis result for the first translated document; the modification-target word or phrase is replaced with a high frequency word in learning data used for learning in the neural network to modify the original document; and the modified original document is translated with the neural network to generate a second translated document.

An example operation may include one or more of transferring a copy of a plurality of revised translation data sets to be added to a software application into a grid structure, each revised translation data set comprising a prompt name in a first field, an interactive voice response (IVR) prompt in a second field, a translation of the IVR prompt into a different language in a third field, and a timestamp in a fourth field, identifying two revised translation data sets in the grid structure that comprise a duplicate prompt names in first fields thereof, deleting an oldest revised translation data set among the two identified translations data sets from the grid structure which has an oldest timestamp, and storing the grid structure without the deleted oldest revised translation data set in a repository.

An example operation may include one or more of transferring a copy of a plurality of revised translation data sets to be added to a software application into a grid structure, each revised translation data set comprising a prompt name in a first field, an interactive voice response (IVR) prompt in a second field, a translation of the IVR prompt into a different language in a third field, and a timestamp in a fourth field, identifying two revised translation data sets in the grid structure that comprise a duplicate prompt names in first fields thereof, deleting an oldest revised translation data set among the two identified translations data sets from the grid structure which has an oldest timestamp, and storing the grid structure without the deleted oldest revised translation data set in a repository.

An example operation may include one or more of transferring a copy of a plurality of revised translation data sets to be added to an IVR application into a grid structure, each revised translation data set comprising a prompt name in a first field, an IVR prompt in a second field, a translation of the IVR prompt into a different language in a third field, and a timestamp in a fourth field, executing, via a processor, an accuracy validation on the plurality of revised translation data sets, wherein, for each revised translation data set, the processor identifies whether a respective translation in a different language in a third field is an accurate translation of a respective IVR prompt in a second field based on attributes of the respective translation and the respective IVR prompt, and displaying results of the accuracy validation via a user interface.

An example operation may include one or more of transferring a copy of a plurality of revised translation data sets to be added to an IVR application into a grid structure, each revised translation data set comprising a prompt name in a first field, an IVR prompt in a second field, a translation of the IVR prompt into a different language in a third field, and a timestamp in a fourth field, executing, via a processor, an accuracy validation on the plurality of revised translation data sets, wherein, for each revised translation data set, the processor identifies whether a respective translation in a different language in a third field is an accurate translation of a respective IVR prompt in a second field based on attributes of the respective translation and the respective IVR prompt, and displaying results of the accuracy validation via a user interface.

Implementations described herein relate to methods, systems, and computer-readable media to localize dynamic content. In some implementations, a computer-implemented method includes receiving visual content associated with a game from a first client locale, the visual content including text being represented at the first client locale by a first language, converting the text to generate translated text in at least a second language associated with a second client locale, and storing the translated text in a database in association with the visual content.