Machine Translation Confidence Flagging via Visual Indicators
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Solution Overview
Problem
Machine translation systems lack the ability to clearly indicate which parts of a translation were generated with high or low confidence, leading to potential misinterpretation by users, as existing confidence indicators do not distinguish between confident and uncertain translations at the word or phrase level.
Innovation Solution
The system flags questionable translations by visually distinguishing them, such as highlighting or changing the color of low-confidence words or phrases, and provides alternative translations for user selection, which can update the translation models to improve future translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine translation systems provide confidence indicators, then users can identify uncertain translations, but the system complexity increases due to multiple models and hypotheses evaluation
Solution Approach 1:
The patent segments the translation confidence evaluation into three distinct indicators: (1) training data frequency of the source word, (2) comparison between top two translation hypotheses, and (3) agreement level among multiple translation models. This segmentation allows the system to provide comprehensive confidence assessment without requiring a single complex evaluation mechanism.
Solution Approach 2:
The translation system is designed to serve multiple functions simultaneously: it performs translation, generates multiple hypotheses, evaluates confidence across different dimensions, and provides visual feedback to users. This multi-functionality is achieved through a unified system architecture that integrates these capabilities rather than requiring separate systems.
2Reliability
If the system flags low-confidence translations, then users can identify questionable translations, but the ease of operation decreases due to additional visual processing
Solution Approach 1:
The patent employs color-coded visual indicators to represent different confidence levels in translations. Words or phrases with low confidence are highlighted using specific colors or shading, allowing users to quickly identify uncertain translations without complex interactions. This visual encoding makes the system easier to use by providing intuitive feedback.
3Measurement precision
If multiple translation hypotheses are generated and compared, then translation accuracy improves, but the processing time increases
Solution Approach 1:
The system generates multiple translation hypotheses and evaluates them to determine confidence levels, but it does not require users to review all hypotheses. Instead, the system performs the necessary comparisons and evaluations partially - enough to determine confidence levels and flag uncertain translations - without presenting all possible alternatives to the user, thus balancing precision with efficiency.
Data Source
AI summary
Exemplary embodiments provide techniques for evaluating when words or phrases of a translation were generated with a low degree of confidence, and conveying this information when the translation is presented. For example, if a source language word is encountered in source material for translation, but the source language word was only encountered a few times (or not at all) in the training data used to train the translation system, then the resulting translation may be flagged as being of low confidence. Other situations, such as the generation of two equally-likely translations, or translation system model disagreement, may also indicate a questionable translation. When the translation is displayed, questionable words and phrases may be flagged, and possible alternative translations may be presented. If one of the alternatives is selected, this information may be used to update the translation system's models in order to improve translation quality in the future.