Dynamic Weight Assignment for Ensemble Translation Models
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Solution Overview
Problem
Existing machine translation models often ignore differences between translation models, leading to inaccurate translation results due to the ensemble method's inability to consider the strengths of individual models in relation to specific sentences or translation fields.
Innovation Solution
A translation method that involves acquiring weights for each translation model based on a pre-trained weighting model and a to-be-translated sentence, allowing for more accurate sentence translation by leveraging the strengths of individual models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ensemble method is used to combine multiple translation models, then translation coverage and robustness are improved, but translation accuracy deteriorates due to inability to consider individual model strengths
Solution Approach 1:
The patent applies local quality by assigning different weights to different translation models based on their performance characteristics and the specific translation task. Instead of uniform treatment, each model receives localized quality adjustment through dynamic weight assignment, allowing the system to leverage individual model strengths in specific contexts while maintaining ensemble robustness
Solution Approach 2:
The patent changes the parameter of model contribution by dynamically adjusting weights assigned to each translation model. This parameter change allows the ensemble system to adapt to different translation scenarios, optimizing accuracy by emphasizing models that perform better in specific language pairs or contexts while maintaining overall reliability
2Device complexity
If uniform weights are assigned to all translation models, then system complexity is reduced, but translation accuracy deteriorates due to ignoring model-specific strengths
Solution Approach 1:
The patent introduces dynamics by making the weight assignment adaptive rather than static. The system dynamically adjusts model weights based on performance metrics, translation pairs, and contextual factors, transforming a complex adaptive system into a manageable framework that automatically optimizes accuracy without requiring manual configuration of each model's contribution
Data Source
AI summary
A translation method, a model training method, apparatuses, electronic devices and storage mediums, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. In an implementation, a weight for each translation model in at least two pre-trained translation models translating a to-be-translated specified sentence is acquired based on the specified sentence and a pre-trained weighting model; and the specified sentence is translating using the at least two translation models based on the weight for each translation model translating the specified sentence.