Multilingual Translation Model Fusion for Accurate Sentence Generation
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Solution Overview
Problem
Conventional translation models suffer from inaccuracies in sentence generation, necessitating improvements in translation quality.
Innovation Solution
A method involving the training of multilingual representation and generation models using monolingual corpora, followed by concatenation with a first translation model, and further training with bilingual corpora to enhance translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional translation model is used, then translation processing can be performed, but the sentence generation accuracy is insufficient
Solution Approach 1:
The patent combines a multilingual representation model and a multilingual generation model with a first translation model to form a unified second translation model. The representation model extracts semantic features from source sentences while the generation model predicts target sentences, and both are integrated with the translation model through concatenation of their parameter sets. This merging of multiple specialized models resolves the accuracy limitation of conventional single models without requiring complete structural redesign.
Solution Approach 2:
The patent divides the translation task into two distinct functional components: a representation model for extracting semantic features and understanding source sentence meaning, and a generation model for predicting and generating target sentences. By segmenting the translation process into these specialized sub-functions, each component can be optimized independently for its specific purpose, thereby improving overall sentence generation accuracy while maintaining manageable complexity through modular design.
2Measurement precision
If multilingual representation model and multilingual generation model are concatenated with first translation model, then translation accuracy is improved, but model complexity increases
Solution Approach 1:
The patent employs multilingual representation and generation models that are trained on monolingual corpora from multiple languages, enabling them to process and understand various languages universally. These models serve multiple functions: they can represent semantics for different languages, generate translations across language pairs, and work together with the translation model. This multi-functionality allows a single integrated architecture to handle diverse translation tasks, improving accuracy across multiple language pairs without requiring separate models for each language combination.
Solution Approach 2:
The patent integrates the representation model and generation model with the translation model by concatenating their parameter sets. Specifically, the parameter set of the representation model and the parameter set of the generation model are concatenated with the parameter set of the translation model to form the parameter set of the second translation model. This parameter-level integration allows the models to work together as a unified system, sharing computational resources and reducing structural complexity compared to fully separate model architectures.
Data Source
AI summary
Embodiments of the present disclosure relate to a translation processing method and apparatus, a device and a medium. The method comprises: generating a multilingual representation model by training according to a monolingual corpus of each language among a plurality of languages, and generating a multilingual generation model according to the monolingual corpus of each language; concatenating the multilingual representation model and the multilingual generation model with a first translation model respectively to generate a target model to be trained; and generating a second translation model by training the target model according to a bilingual corpus among the plurality of languages, and performing translation processing on target information to be processed, according to the second translation model.