Confidence Estimation for Machine Translation Post Edit Review
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Solution Overview
Problem
Machine translation post edit review processes are cumbersome and lack efficient methods for estimating translation accuracy, particularly in hybrid translations involving both machine translation and translation memory, which complicates the identification of complex segments for reviewers.
Innovation Solution
The system calculates confidence estimations for machine translated segments by comparing them to benchmark values and associates each segment with a color in a graphical format, allowing reviewers to visualize and prioritize segments based on complexity, enhancing the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If translation memory managers are used to estimate accuracy by comparing to database translations, then measurement precision of translation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies color coding to visually represent confidence estimations of translated segments. Each segment is assigned a color (e.g., green, yellow, red) corresponding to different confidence levels, enabling reviewers to quickly identify segments requiring attention without complex analysis of each segment individually.
Solution Approach 2:
The system creates a visual copy or representation of the confidence estimation data through color-coded segments. Instead of presenting raw confidence scores or requiring detailed segment analysis, the system generates a simplified visual representation that mirrors the underlying accuracy data, making it immediately interpretable by reviewers.
2Loss of energy
If direct estimation of post editing complexity without fuzzy matching algorithms is used, then loss of energy is reduced, but measurement precision of translation accuracy deteriorates
Solution Approach 1:
The system applies fuzzy matching algorithms selectively rather than uniformly to all segments. By using confidence algorithms only where needed (e.g., for segments with lower confidence scores or higher complexity), the system reduces overall computational cost while maintaining sufficient measurement precision for segments that require it.
Solution Approach 2:
The patent implements different levels of accuracy estimation for different segments based on their characteristics. High-confidence segments with exact matches receive simpler estimation, while low-confidence or complex segments undergo more rigorous analysis using fuzzy matching and confidence algorithms, optimizing the balance between computational cost and measurement precision.
3Measurement precision
If post editing analysis is performed on each segment, then measurement precision of translation accuracy is improved, but loss of time increases
Solution Approach 1:
The color-coded visual representation allows reviewers to immediately identify segments requiring detailed analysis. By presenting confidence estimations in a visually intuitive format, reviewers can prioritize their time on segments with lower confidence scores (e.g., red or yellow segments) while quickly accepting high-confidence segments (e.g., green segments) without detailed review.
Solution Approach 2:
The system performs preliminary confidence estimation and color-coding of all segments before the reviewer begins detailed analysis. This preliminary action pre-sorts segments by their likely need for review, enabling reviewers to efficiently navigate to segments requiring attention without performing exhaustive analysis on every segment from scratch.
Data Source
AI summary
Systems and methods for enhancing machine translation post edit review processes are provided herein. According to some embodiments, methods for displaying confidence estimations for machine translated segments of a source document may include executing instructions stored in memory, the instructions being executed by a processor to calculate a confidence estimation for a machine translated segment of a source document, compare the confidence estimation for the machine translated segment to one or more benchmark values, associate the machine translated segment with a color based upon the confidence estimation for the machine translated segment relative to the one or more benchmark values, and provide the machine translated segment having the color in a graphical format, to a client device.