Manual Phrase Alignment for Statistical Machine Translation Models
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Solution Overview
Problem
Statistical machine translation systems face challenges in achieving high-quality word alignments with limited training data, as existing methods rely heavily on large amounts of parallel sentences, making rapid development impractical.
Innovation Solution
Implementing manual phrase-level alignment using a graphical user interface tool, where bilingual speakers select and align sentence pairs, and incorporating these alignments into the training process to improve automatic word alignment models, while also using semi-supervised and supervised learning techniques to enhance the quality of word alignment models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of parallel sentences are collected for training, then translation quality improves, but the time and resources required for data collection increase significantly
Solution Approach 1:
The patent performs preliminary manual phrase-level alignment on a small subset of training data before automated processing. This preliminary action creates high-quality seed alignment data that guides subsequent automated word alignment, allowing the system to achieve good translation quality without collecting and processing large amounts of raw parallel sentences
Solution Approach 2:
The patent introduces manually aligned phrase pairs as an intermediary between raw parallel sentences and the final translation model. These manually aligned phrases serve as a bridge that guides the automated word alignment algorithm, enabling it to produce accurate alignments from limited training data
2Measurement precision
If manual word-level alignment is performed, then alignment quality improves, but the complexity and time required for manual annotation increase
Solution Approach 1:
The patent segments the alignment task from the word level to the phrase level. Instead of requiring annotators to align individual words, they only need to identify and align complete phrases, which are more easily recognizable and require less linguistic expertise. This segmentation dramatically simplifies the manual annotation process while maintaining high alignment quality
Solution Approach 2:
The patent changes the granularity parameter of the alignment task from word-level to phrase-level. This parameter change makes the manual alignment task more manageable for bilingual speakers without extensive linguistic training, reducing the complexity and time required for annotation
Data Source
AI summary
The present invention adopts the fundamental architecture of a statistical machine translation system which utilizes statistical models learned from the training data and does not require expert knowledge for rule-based machine translation systems. Out of the training parallel data, a certain amount of sentence pairs are selected for manual alignment. These sentences are aligned at the phrase level instead of at the word level. Depending on the size of the training data, the optimal amount for manual alignment may vary. The alignment is done using an alignment tool with a graphical user interface which is convenient and intuitive to the users. Manually aligned data are then utilized to improve the automatic word alignment component. Model combination methods are also introduced to improve the accuracy and the coverage of statistical models for the task of statistical machine translation.