Hybrid Syntax-Phrase Machine Translation Model
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Solution Overview
Problem
Existing statistical machine translation systems, such as phrase-based SMT and syntax-based SMT, face challenges in effectively changing long-distance word order and achieving natural translations, with phrase-based systems improving fluency but failing to alter sentence structure and syntax-based systems relying heavily on parsers and constrained translation knowledge.
Innovation Solution
A machine translation device and method that extracts syntax conversion knowledge and word translation knowledge from parallel corpora, learns conversion probabilities, and generates target sentences through a combined syntax conversion model and word translation model, allowing for real-time decoding and improved long-distance word rearrangement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If phrase-based SMT is used to improve fluency of consecutive word translation, then translation fluency is improved, but long-distance word order cannot be changed effectively
Solution Approach 1:
The patent combines phrase-based SMT and syntax-based SMT into a unified system that processes both consecutive phrases and long-distance word order transformations simultaneously. The syntax converter generates multiple candidate sentences with different word orders, while the phrase translator handles fluent phrase-level translation, and both results are integrated through a scoring mechanism.
Solution Approach 2:
The translation process is segmented into distinct stages: syntax conversion stage that handles word order rearrangement, phrase translation stage that handles fluent translation, and integration stage that combines results. This segmentation allows each component to specialize in specific tasks while working together to solve the overall translation problem.
2Manufacturing precision
If syntax-based SMT is used to change long-distance word order, then word order accuracy is improved, but translation knowledge is constrained to grammatical phrase units resulting in simple word-to-word translation
Solution Approach 1:
The system merges syntax-based word order conversion with phrase-based translation knowledge by integrating the output of the syntax converter with the phrase translation model. This allows the system to maintain accurate word order while preserving rich translation knowledge from phrase-level parallel corpora.
Solution Approach 2:
The phrase translation model acts as an intermediary that bridges syntax conversion and final translation. It takes the syntactically converted source sentence and applies phrase-level translation knowledge to generate natural target sentences, preventing degradation to simple word-to-word translation.
3Adaptability or versatility
If syntax-based SMT is used to model tree-to-tree conversion, then long-distance word order is easily changed, but dependency on parser performance reduces translation knowledge availability
Solution Approach 1:
The system segments the translation process so that syntax conversion is one optional path among others. The phrase translation path provides an alternative that does not depend on parser performance, allowing the system to maintain reliability while still offering long-distance word order change when needed.
Solution Approach 2:
The system changes the parameter of translation approach by offering multiple paths (phrase-based, syntax-based, hybrid) with different levels of parser dependency. The scoring mechanism dynamically selects the most appropriate path based on the specific translation context and available resources.
4Device complexity
If a unified translation model is used to process both phrase and syntax conversion, then system complexity is reduced, but the ability to handle both consecutive and long-distance transformations is compromised
Solution Approach 1:
The system is segmented into specialized components (syntax converter, phrase translator, scorer) that work together in a coordinated pipeline. Each component has a specific function, and their integration maintains manageable complexity while achieving versatile transformation capability through the hybrid approach.
Data Source
AI summary
The present invention relates to statistical machine translation, and provides a machine translation device and a machine translation method that acquire a creation probability for a target language from a single corpus while extracting respective conversion probabilities by extracting syntax conversion knowledge and word translation knowledge from a parallel corpus, model a weighted translation model by allowing each of the conversion knowledge and each of the probabilities to learn using a translation model learning device, and generate a target sentence through decoding processes of a syntax converter and a word translator by applying the translation model to a source sentence input in real time, thereby resolving disadvantages of the existing phrase-based SMT and syntax-based SMT and combining advantages thereof.