Modular Machine Translation Model with Dynamic Expert Selection
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Solution Overview
Problem
Current machine translation models face challenges in efficiently handling multiple domain translations due to large model sizes and the need for joint training of all modules, leading to high resource consumption and reduced translation quality.
Innovation Solution
The proposed solution involves decomposing training for each module in the machine translation model, where the codec module is fixed, followed by training the data distribution prediction module, and then the mixture expert module, allowing for independent updates and reducing the need for highly coupled training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If joint training of all modules is used, then translation quality may be improved, but training costs and resource consumption increase
Solution Approach 1:
The patent divides the machine translation model into separate modules (codec module, data distribution prediction module, mixture expert module) that can be trained independently. The codec module is trained first and fixed, then the data distribution prediction module is trained, and finally the mixture expert module is trained. This segmentation allows each module to be trained with appropriate computational resources without requiring joint training of all modules, thereby reducing overall training resource consumption while maintaining translation quality.
2Adaptability or versatility
If large model size is used to handle multiple domains, then translation versatility improves, but device complexity and resource consumption increase
Solution Approach 1:
The patent implements a dynamic expert selection mechanism where the mixture expert module can dynamically select from multiple candidate experts based on the input data characteristics. Instead of using a large static model that handles all domains simultaneously, the system activates only the necessary experts for each translation task. This dynamic approach enables multi-domain translation capability while keeping the active model size manageable, reducing device complexity and resource consumption.
Solution Approach 2:
The codec module serves as a universal base model that can handle multiple translation tasks across different domains. The data distribution prediction module provides universal domain classification capability. The mixture expert module contains multiple specialized experts that can be selected based on the specific domain requirements. This universal foundation combined with specialized experts allows the system to achieve multi-domain translation versatility without requiring a completely large model for each domain.
3Reliability
If all modules are highly coupled for joint training, then translation accuracy improves, but ease of maintenance and updates deteriorates
Solution Approach 1:
The patent segments the model into independently trainable modules with defined interfaces. The codec module, data distribution prediction module, and mixture expert module can be updated separately without requiring retraining of other modules. This modular architecture maintains translation accuracy through careful interface design while significantly improving ease of maintenance and updates, as only the relevant module needs to be updated rather than the entire model.
Solution Approach 2:
The patent allows for parameter changes in individual modules without affecting the entire model. The mixture expert module can update its expert parameters independently, and the data distribution prediction module can update its classification parameters separately. This parameter-level independence maintains translation accuracy through proper parameter initialization and matching while enabling easy maintenance, as updates can be applied locally without propagating through the entire highly coupled model.
Data Source
AI summary
A method performed by an electronic device comprises acquiring information to be translated. The method includes determining, based on the information to be translated, a target domain adapter from a plurality of candidate domain adapters, the target domain adapter corresponding to the information to be translated, each candidate domain adapter from the plurality of candidate domain adapters corresponding to at least one domain. The method includes obtaining, based on the target domain adapter corresponding to the information to be translated, a translation result corresponding to the information to be translated.