Subtitle Localization via Logical Sentence Segmentation
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Solution Overview
Problem
Current machine translation techniques are inadequate for accurately translating idioms and complex sentence structures, leading to errors in subtitle localization for streaming video content, which can significantly impact the quality and utility of translated software, documents, and services.
Innovation Solution
A subtitle localization system that organizes source-language subtitle events into logical sentences, translates them into target-language text using machine translation and translation memory applications, and generates high-quality translation suggestions, even for incomplete sentences, by combining or separating text to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation algorithms are used to translate subtitle text, then translation speed and productivity are improved, but translation accuracy deteriorates due to inability to accurately translate idioms and complex sentence structures
Solution Approach 1:
The system segments the translation process into multiple stages: machine translation generates initial translations, then human translators review and edit specific portions. This segmentation allows automated speed benefits while preserving human accuracy for problematic segments like idioms and complex sentences.
Solution Approach 2:
The system implements feedback loops where human translator corrections are fed back into the machine translation process. This feedback mechanism allows the system to learn from errors and improve accuracy over time while maintaining high productivity through automated initial translations.
2Manufacturing precision
If manual translation is used to ensure high translation quality, then translation accuracy is improved, but translation time and cycle time increase significantly
Solution Approach 1:
Instead of requiring complete manual translation of all content, the system applies partial manual action only where needed - specifically for reviewing and editing machine-translated segments that contain idioms, complex structures, or potential errors. This reduces overall translation time while maintaining accuracy for critical segments.
3Extent of automation
If conventional machine translation techniques are applied to subtitle events, then automation extent is improved, but translation quality deteriorates when subtitle events represent only fractions of complete logical sentences
Solution Approach 1:
The system merges multiple subtitle events that represent fractions of complete logical sentences into unified translation units. By combining these fragmented segments, the machine translation algorithm can process complete contextual meanings, improving translation quality while maintaining automation.
Data Source
AI summary
One embodiment of the present disclosure sets forth a technique for generating translation suggestions. The technique includes receiving a sequence of source-language subtitle events associated with a content item, where each source-language subtitle event includes a different textual string representing a corresponding portion of the content item, generating a unit of translatable text based on a textual string included in at least one source-language subtitle event from the sequence, translating, via software executing on a machine, the unit of translatable text into target-language text, generating, based on the target-language text, at least one target-language subtitle event associated with a portion of the content item corresponding to the at least one source-language subtitle event, and generating, for display, a subtitle presentation template that includes the at least one target-language subtitle event.