Neural Machine Translation Evaluation via Statistical Phrase Tables
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Solution Overview
Problem
Neural network-based machine translation (NMT) systems are opaque, making it difficult to evaluate and correct translation errors, and require significant resources for training, which can be wasteful if the models are already strong in certain areas.
Innovation Solution
The use of statistical phrase tables and quality metrics to evaluate and improve NMT systems by providing confidence scores and identifying areas of model weakness early in the development process, allowing for the selection of alternative translation techniques that may require fewer resources and provide more accurate translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network based machine translation is used, then translation quality is improved, but system opacity increases making evaluation and error correction difficult
Solution Approach 1:
The patent introduces statistical phrase tables as an intermediary component between the neural network model and the translation output. These phrase tables provide interpretable statistical information about translation preferences and confidence levels, allowing evaluators to understand and assess the NMT system's decision-making process without directly analyzing the complex neural network internals.
Solution Approach 2:
The system implements feedback mechanisms by using the statistical phrase tables to evaluate translation quality and identify errors. This feedback loop enables continuous improvement of the NMT model by highlighting specific areas where translations fail, allowing targeted retraining and model optimization.
2Measurement precision
If neural network models are trained extensively, then translation accuracy improves, but resource consumption increases
Solution Approach 1:
The patent applies preliminary action by using statistical phrase tables to evaluate and identify model weaknesses before extensive retraining is performed. This preliminary evaluation allows the system to focus training resources only on specific areas that need improvement, rather than performing blanket retraining across the entire model.
Solution Approach 2:
The system changes parameters by adjusting training focus based on feedback from statistical phrase table analysis. Instead of uniform training across all model parameters, the system identifies specific parameter sets or translation scenarios that require improvement and directs training resources accordingly, reducing overall resource consumption.
3Productivity
If alternative translation techniques are selected based on quality metrics, then resource efficiency improves, but translation quality may decrease
Solution Approach 1:
The patent implements a dynamic translation system that selects between different translation techniques (statistical machine translation, neural machine translation, or rule-based translation) based on real-time quality metrics and confidence levels from statistical phrase tables. This dynamic selection ensures that the most appropriate method is used for each specific translation task, optimizing both quality and resource efficiency.
Solution Approach 2:
The system applies local quality by using statistical phrase tables to identify specific translation scenarios or phrase types where NMT performs well versus where alternative methods are superior. Instead of uniformly applying one translation method across all contexts, the system tailors the translation approach to the specific local requirements of each translation task.
Data Source
AI summary
Based on a candidate set of translations produced by a neural network based machine learning model, a mapping data structure such as a statistical phrase table is generated. The mapping data structure is analyzed to obtain a quality metric of the neural network based model. One or more operations are initiated based on the quality metric.