Abstract: A computer processing system is provided for enhancing video-based language learning. The system includes a video server for storing videos that use one or more languages to be learned. The system further includes a video metadata database for storing translations of sentences uttered in the videos, character profiles of characters appearing in the videos, and mappings between the sentences and a learner profile. The system also includes a learner profile database for storing learner profiles. The system additionally includes a semantic analyzer and matching engine for finding, for at least a given video and a given learner, alternative sentences for and responsive to the translations of the sentences uttered in the given video that conflict with a respective learner profile for the given learner. The computer processing system further includes a presentation system for playing back the given video and providing the alternative sentences to the given learner.

Abstract: A computer processing system is provided for enhancing video-based language learning. The system includes a video server for storing videos that use one or more languages to be learned. The system further includes a video metadata database for storing translations of sentences uttered in the videos, character profiles of characters appearing in the videos, and mappings between the sentences and a learner profile. The system also includes a learner profile database for storing learner profiles. The system additionally includes a semantic analyzer and matching engine for finding, for at least a given video and a given learner, alternative sentences for and responsive to the translations of the sentences uttered in the given video that conflict with a respective learner profile for the given learner. The computer processing system further includes a presentation system for playing back the given video and providing the alternative sentences to the given learner.

Abstract: A computer processing system is provided for enhancing video-based language learning. The system includes a video server for storing videos that use one or more languages to be learned. The system further includes a video metadata database for storing translations of sentences uttered in the videos, character profiles of characters appearing in the videos, and mappings between the sentences and a learner profile. The system also includes a learner profile database for storing learner profiles. The system additionally includes a semantic analyzer and matching engine for finding, for at least a given video and a given learner, alternative sentences for and responsive to the translations of the sentences uttered in the given video that conflict with a respective learner profile for the given learner. The computer processing system further includes a presentation system for playing back the given video and providing the alternative sentences to the given learner.

Abstract: Software translation quality and efficiency are improved by providing user interface (UI) context for translators. Unicode symbols are used to uniquely tag user-visible strings from the source code and into resource files. Those strings include titles, product names, error messages, strings in images and any other text that may be present on the user interface. Once the ‘pseudo’ resource files are integrated into a build, automation is run to gather screenshots of the application. Image recognition is then used to link screenshots of the UI in which a resource file string appears, such that screenshots will be brought forward and displayed to the translator when working on translating the user-visible strings of the software being localized.

Abstract: Software translation quality and efficiency are improved by providing user interface (UI) context for translators. Unicode symbols are used to uniquely tag user-visible strings from the source code and into resource files. Those strings include titles, product names, error messages, strings in images and any other text that may be present on the user interface. Once the ‘pseudo’ resource files are integrated into a build, automation is run to gather screenshots of the application. Image recognition is then used to link screenshots of the UI in which a resource file string appears, such that screenshots will be brought forward and displayed to the translator when working on translating the user-visible strings of the software being localized.