Translation Engine Selection via Targeted Probes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional translation engines lack accuracy and granularity in translation, often resulting in loss of meaning and incorrect translations, as they are evaluated solely based on total output rather than at a word, phrase, or sentence level.
Innovation Solution
A computer-implemented method that uses targeted probes to segment input text into granular segments, applies each segment to the best-suited translation engine, and combines outputs for accurate translation, employing Siamese RNN networks and LSTM phrase reconstruction to determine the optimal translation engine for each segment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional translation engines are used to translate text, then translation output is produced quickly, but translation accuracy and semantic preservation deteriorate
Solution Approach 1:
The patent segments the input text into multiple sentences and evaluates each sentence independently using different translation engines. This allows the system to select the best engine for each sentence based on probe-based accuracy assessment, thereby improving overall translation accuracy while maintaining efficiency through parallel processing of segmented text.
2Measurement precision
If translation engines are evaluated based on total output, then evaluation process is simple, but measurement precision deteriorates
Solution Approach 1:
The patent divides the translation evaluation process into sentence-level segments, creating probes for each sentence to assess translation quality independently. This granular evaluation approach enables precise measurement of translation accuracy at the sentence level rather than only at the total output level, improving measurement precision despite increased evaluation complexity.
Solution Approach 2:
The system implements feedback mechanisms by comparing translated sentences against reference translations and using accuracy metrics to inform engine selection. This feedback loop allows the system to learn from evaluation results and continuously improve its engine selection strategy, balancing measurement precision with manageable complexity.
3Adaptability or versatility
If a single translation engine is used for all text, then device complexity is low, but adaptability to different translation contexts deteriorates
Solution Approach 1:
The patent implements a dynamic engine selection mechanism that chooses different translation engines based on the specific characteristics of each sentence and probe-based accuracy assessment. This dynamic approach allows the system to adapt to different translation contexts and sentence types, improving versatility while managing complexity through automated selection algorithms.
Solution Approach 2:
The system applies different translation engines to different sentences based on local characteristics and accuracy requirements. Each sentence can be translated by the most suitable engine for its specific context, rather than applying a uniform engine to all text. This local quality approach improves adaptability while keeping system complexity manageable through context-aware selection.
Data Source
AI summary
A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.