Machine Translation Service Framework with Hybrid Model Gap Correction
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Solution Overview
Problem
Current machine translation technologies face challenges in achieving high accuracy without compromising speed, particularly in handling translation string gaps and inaccuracies across different models, leading to inefficiencies in translation processes.
Innovation Solution
A novel machine translation as a service (MTaS) framework that leverages a hybrid approach combining statistical, rule-based, and neural machine translation models, enabling real-time translation with automated correction and fine-tuning mechanisms, and crowd-sourced feedback to improve translation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple translation models (SMT, NMT, RBMT) are applied iteratively to improve accuracy, then translation accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The translation process is segmented into distinct phases: initial translation using SMT model, accuracy verification, and selective re-translation using NMT model only when gaps or errors are detected. This segmentation allows the system to maintain high accuracy through multi-model approach while reducing overall processing time by avoiding unnecessary iterative translations of all text strings.
Solution Approach 2:
The system incorporates feedback mechanisms where translation outputs are verified for accuracy and completeness. When gaps or inaccuracies are detected in the SMT translation, the system feeds this information back to trigger selective NMT re-translation of only the problematic segments. This feedback loop enables accuracy improvement without requiring complete re-processing of entire text strings.
2Measurement precision
If translation string gaps are identified and corrected through iterative translation releases, then translation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-verification of translation outputs by automatically detecting gaps and inaccuracies in the translated text. The verification process is integrated into the translation pipeline itself, allowing the system to identify and correct its own errors without requiring external intervention. This self-service capability manages system complexity by automating the gap-detection and correction workflows.
Solution Approach 2:
The translation system dynamically adjusts its behavior based on detected translation quality. When gaps or errors are identified in automated verification, the system dynamically switches from SMT-only mode to a hybrid mode that applies NMT model specifically to correct the identified issues. This dynamic adaptation allows the system to handle complexity flexibly by activating additional computational resources only when needed.
3Productivity
If real-time translation is performed with automated correction mechanisms, then translation speed is maintained, but computational resources required increase
Solution Approach 1:
Instead of applying all translation models to entire text strings, the system applies partial action by using SMT for initial translation of all content, then applying NMT only to the partial segments that require correction. This selective application of computational resources maintains real-time translation capability while significantly reducing overall energy consumption compared to applying all models to all text.
Solution Approach 2:
The system performs preliminary translation using the faster SMT model to establish an initial output, then uses more computationally intensive NMT model only for corrective actions on identified gaps. This preliminary action approach allows the system to maintain high translation throughput while using additional computational resources efficiently only when necessary to correct errors.
Data Source
AI summary
Disclosed are systems and methods for a novel machine translation as a service (MTaS) framework that enables real-time machine translation to realize improved accuracy without degrading the speed in which a translator can operate. The disclosed translator technology can provide an improved, computationally efficient and accurate system that can improve how translations are provided, which can improve how translation-based processes are performed. The disclosed framework enables translation string gaps between translated releases to be identified, filled and/or corrected. Such automated and/or selected translation correction, modification and/or fine-tuning can be enabled via any type of translation model and/or model combination. The disclosed framework effectively enables a crowd-sourced translation to be performed, which can include, but is not limited to, translations from a plurality of translator models, repetitive translations via a translation model and/or translations that account for user feedback from particular translation releases, and the like, or some combination thereof.