Statistical Machine Translation Using Syntactic Chunk Alignment
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Solution Overview
Problem
Conventional machine translation systems face challenges in translating phrases, restructuring sentences for syntactical reasons, and translating non-adjacent words into single words or phrases in the target sentence, as they rely on word-based decisions that do not effectively handle these translational complexities.
Innovation Solution
The proposed method involves detecting syntactic chunks in the source sentence, assigning labels, aligning these chunks with corresponding chunks in the target sentence, and translating words based on these labels, using a linguistic statistical translation process that includes language and translation models, along with chunk mapping and reordering techniques to improve translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If word-based translation decisions are used, then the translation process is simple, but the ability to handle complex sentence structures and syntactical relationships deteriorates
Solution Approach 1:
The patent segments the translation process into multiple stages: syntactic chunk detection, label assignment, alignment to target language chunks, and word-level translation. This segmentation allows the system to handle complex sentence structures by processing them in manageable parts rather than attempting word-based translation of entire sentences at once.
Solution Approach 2:
The patent introduces a syntactic dimension to the translation process by detecting and labeling syntactic chunks (noun phrases, verb phrases, etc.) before translation. This adds a structural layer beyond simple word sequences, enabling the system to maintain syntactical relationships in the target language while translating.
2Reliability
If syntactic chunk detection and alignment is implemented, then translation accuracy for complex structures is improved, but the translation process complexity increases
Solution Approach 1:
The patent performs preliminary syntactic analysis by detecting chunks and assigning labels before the actual translation process. This preliminary action prepares the input data in a structured format that facilitates more accurate translation of complex structures, separating the analytical phase from the translation phase.
Solution Approach 2:
The patent introduces syntactic chunk labels as an intermediary representation between the source language input and the target language output. These labels serve as a mediator that captures syntactical information, enabling the translation model to preserve structural relationships without directly processing complex syntactical rules during translation.
3Productivity
If conventional word-based translation is used, then the translation process is fast, but the handling of non-adjacent words and phrase restructuring deteriorates
Solution Approach 1:
The patent segments sentences into syntactic chunks (noun phrases, verb phrases, prepositional phrases, etc.) that can be independently processed. This segmentation allows the system to handle non-adjacent words by grouping them into meaningful units, and to restructure phrases according to target language syntax while maintaining translation speed through efficient chunk-level processing.
Data Source
AI summary
A method includes detecting a syntactic chunk in a source string in a first language, assigning a syntactic label to the detected syntactic chunk in the source string, mapping the detected syntactic chunk in the source string to a syntactic chunk in a target string in a second language, said mapping based on the assigned syntactic label, and translating the source string into a possible translation in the second language.