Machine Translation System User Engagement Feedback Loop
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine translation systems face challenges in determining the most accurate translation due to the limitations of the BLEU score, which is expensive, time-consuming, and fails to capture whole sentence-level meaning and grammatical correctness, especially in languages with unclear word-level boundaries.
Innovation Solution
A machine translation system that distributes multiple candidate translations to different user groups, measures user engagement, and updates its models to favor the most engaged translations, creating a feedback loop to continuously improve translation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the BLEU score is used to evaluate machine translations, then translation quality can be measured, but the evaluation process becomes expensive and time-consuming
Solution Approach 1:
The patent implements a feedback mechanism where user engagement metrics (clicks, likes, shares) with translated content are collected and used to iteratively retrain and improve the translation model. This continuous feedback loop replaces the static, time-consuming BLEU score evaluation with a dynamic, real-world performance-based assessment that automatically improves translation quality over time without requiring manual re-evaluation.
2Manufacturing precision
If multiple reference translations are used to accommodate different correct translations, then translation accuracy improves, but the cost and time for producing reference translations increases
Solution Approach 1:
The system uses real user interactions with translated content to automatically identify and learn preferred translations. Instead of requiring multiple human-generated reference translations, the system self-improves by observing which translations users actually engage with, allowing a single reference translation to suffice while still achieving high accuracy through continuous learning from user behavior.
3Measurement precision
If the BLEU score is used to evaluate translations, then translation quality can be assessed, but it fails to capture whole sentence-level meaning and grammatical correctness
Solution Approach 1:
The patent transitions from word-level BLEU score evaluation to sentence-level user engagement evaluation. By measuring user interactions (clicks, likes, shares) at the sentence or article level rather than comparing individual word translations, the system captures holistic meaning and grammatical correctness that BLEU scores miss, while maintaining measurement precision through real-world user feedback.
Data Source
AI summary
Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.