Neural Machine Translation Clause Segmentation and Semantic Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional autoregressive neural machine translation technologies translate sentences word by word, leading to slow translation speeds and potential translation errors.
Innovation Solution
Divide the to-be-translated sentence into pre-defined clauses, translate each clause separately, and combine the results semantically to obtain a target translation sentence, allowing for parallel processing and improved translation speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autoregressive neural machine translation is used to translate sentences word by word, then translation quality can be maintained, but translation speed becomes slow
Solution Approach 1:
The patent divides the source sentence into multiple clauses based on semantic units rather than translating word by word. Each clause is independently processed and translated in parallel, which maintains translation quality while significantly improving translation speed. This segmentation approach allows the system to process multiple words simultaneously instead of sequentially.
2Reliability
If sentences are translated word by word using conventional methods, then translation process is simple, but translation errors increase and quality decreases
Solution Approach 1:
The patent segments the sentence into semantic clauses and processes each independently, which reduces translation errors by maintaining contextual integrity within each clause. The segmentation module divides the sentence based on semantic units, and the translation module then processes each clause separately, improving overall accuracy.
Solution Approach 2:
The patent incorporates a feedback mechanism where the combining module receives translation results from multiple clauses and adjusts the final translation based on semantic consistency. This feedback loop ensures that translation errors in individual clauses are corrected through semantic fusion, improving overall translation reliability.
3Productivity
If clauses are translated separately and combined semantically, then translation speed increases, but processing complexity increases
Solution Approach 1:
The patent uses a segmentation module that divides the source sentence into clauses based on semantic units, making the complex translation process more manageable. This segmentation allows parallel processing of multiple clauses while maintaining semantic integrity, thus improving speed without excessive complexity.
Solution Approach 2:
The patent merges the translation results of multiple clauses through a combining module that performs semantic fusion. This merging process integrates the translated clauses into a coherent final translation while maintaining the benefits of parallel processing. The combining step consolidates multiple translation results into a single output, managing processing complexity effectively.
Data Source
AI summary
An electronic device obtains a to-be-translated sentence. The electronic device divides the to-be-translated sentence into a preset quantity of clauses. The electronic device separately translates each of the clauses to obtain a respective translation result corresponding to each of the clauses. The electronic device combines the respective translation results corresponding to each of the clauses according to semantics to obtain a target translation sentence corresponding to the to-be-translated sentence.