Dynamic Auto-Suggest Dictionary Generation for Translation Memory
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Solution Overview
Problem
Current machine-assisted translation technologies have limited reuse of previously translated text due to high granularity levels, such as sentences or paragraphs, and require substantial human effort for maintaining term bases and dictionaries.
Innovation Solution
The technology dynamically generates auto-suggest dictionary data from translation data stored in memory, which includes reliable translations of source content in a target language, and transmits this data to a remote device for use in natural language translation, reducing human input and improving translation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If translation memories leverage existing translations on the sentence or paragraph level, then translation quality can be maintained, but the amount of re-use possible is limited due to the relatively low chance of a whole sentence or paragraph matching the source text
Solution Approach 1:
The patent segments translation units from the traditional sentence or paragraph level down to smaller linguistic units such as phrases, clauses, or even individual words. This segmentation increases the probability of finding matching translation units in the translation memory, thereby improving the amount of reusable translation content while maintaining quality through selective reuse of segmented units.
Solution Approach 2:
The patent introduces a new dimension of granularity control in translation memory operations, allowing dynamic adjustment between different levels of translation unit segmentation. This enables the system to optimize between reuse quantity and matching accuracy by selecting appropriate granularities based on the specific translation context and requirements.
2Productivity
If a term base or multilingual dictionary is built up from previous translations, then leverage of previous translations is improved, but substantial effort and human input from skilled terminologists are required for development and maintenance
Solution Approach 1:
The patent implements self-service mechanisms where the translation memory system automatically extracts, processes, and organizes translation units from translated content without requiring manual intervention from terminologists. The system autonomously builds and maintains term bases and dictionaries by leveraging the translated content itself, thereby improving leverage of previous translations while eliminating substantial human effort for creation and maintenance.
Solution Approach 2:
The patent employs automated processes to discard raw translation data and recover only the valuable terminological elements, automatically filtering and organizing translation units into structured term bases. This automated discarding and recovering process replaces manual terminological extraction, reducing human effort while maintaining high leverage of translated content.
3Extent of automation
If extraction technology is used to automatically extract term candidates from existing resources, then human input required is reduced, but the human effort required in creating and maintaining term bases can still be considerable
Solution Approach 1:
The patent performs preliminary automated extraction of term candidates from translation memory content before the actual term base creation process. By pre-processing and identifying potential terminology in advance, the system reduces the subsequent human effort required for term base creation and maintenance, while maximizing the extent of automation in the overall process.
Solution Approach 2:
The patent implements feedback mechanisms where extracted term candidates are automatically validated, refined, and integrated into the term base with minimal human oversight. The system uses feedback from translation usage patterns to automatically improve term base quality, reducing the need for continuous manual creation and maintenance efforts while enhancing automation.
Data Source
AI summary
The present technology dynamically generates auto-suggest dictionary data from translation data stored in memory at a server. The auto-suggest dictionary data may be transmitted to a remote device by the server for use in language translation. The auto-suggest dictionary data may be transferred as part of a package which includes content to be translated, translation meta-data, and various other data. The auto-suggest dictionary data may be generated at a first computing device, periodically or in response to an event, from translation data stored in memory. The auto-suggest dictionary may be transferred to a remote device along with content to be translated and other data, as part of a package, for use in translation of the content at the remote device.