Pairwise Ranking for Statistical Machine Translation Parameter Tuning
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Solution Overview
Problem
Current statistical machine translation systems, such as MERT, face scalability issues when dealing with high-dimensional feature spaces, limiting their ability to effectively discriminate between candidate translations and hindering feature development innovation.
Innovation Solution
The approach involves casting the parameter tuning process as a ranking problem, using pair-wise ranking and linear binary classification to adjust weighting values based on BLEU scores and loss functions, allowing for the selection of more relevant candidate translation units, and implementing this within existing MT frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MERT algorithms are used for parameter tuning, then translation quality can be improved, but scalability to high-dimensional feature spaces deteriorates
Solution Approach 1:
The patent segments the parameter tuning process into pairwise comparisons of candidate translation units. Instead of optimizing all parameters simultaneously in high-dimensional space, the system divides the problem into discrete pairs of candidates and tunes parameters through iterative pairwise ranking, making the tuning process scalable to high-dimensional feature spaces while maintaining translation quality
Solution Approach 2:
The patent transforms the parameter tuning problem from optimizing weights directly in high-dimensional feature space to ranking candidate translation units based on their scores. This dimensional transformation allows the system to handle millions of features by working in the space of candidate rankings rather than raw feature dimensions, resolving the scalability issue while preserving translation quality
2Reliability
If more features are added to improve translation discrimination, then translation effectiveness improves, but system complexity increases
Solution Approach 1:
The patent implements pairwise ranking that can handle excessive numbers of features (millions of features) without requiring complete optimization of all feature interactions. By focusing on pairwise comparisons and iterative tuning, the system achieves effective translation discrimination with partial optimization at each step, avoiding the complexity of full high-dimensional optimization while maintaining effectiveness
3Measurement precision
If exhaustive search is performed to find best translation, then translation accuracy improves, but computational time increases
Solution Approach 1:
The patent performs preliminary action by pre-computing scores for candidate translation units using the trained parameters before the actual translation selection. The pairwise ranking process establishes a scoring framework in advance, allowing the system to quickly rank and select the best translation candidate without performing exhaustive search at translation time, thus maintaining accuracy while reducing computational time
Data Source
AI summary
A method for tuning translation parameters in statistical machine translation based on ranking of the translation parameters is disclosed. According to one embodiment, the method includes sampling pairs of candidate translation units from a set of candidate translation units corresponding to a source unit, each candidate translation unit corresponding to numeric values assigned to one or more features, receiving an initial weighting value for each feature, comparing the pairs of candidate translation units to produce binary results, and using the binary results to adjust the initial weighting values to produce modified weighting values.