Super-large-scale multi-language neural machine translation method based on MoE system architecture

Abstract

The invention relates to the technical field of natural language processing, and discloses a super-large-scale multi-language neural machine translation method based on a MoE system architecture, which comprises the following steps: S1, an encoder and a decoder adopt a Transform-based architecture, and a MoE layer is embedded in the Transform-based architecture; s2, the MoE layer comprises a plurality of expert models for specific language pair or language characteristic optimization; s3, designing a dynamic gating mechanism, and selecting the most suitable expert model for prediction according to input data features; s4, introducing a load balancing mechanism to ensure that all expert models are subjected to balanced training and use; s5, performing model training by adopting a combined strategy of data parallelism and MoE parallelism; and S6, compressing and optimizing the model by applying knowledge distillation and quantification technologies. The invention discloses a super-large-scale multi-language neural machine translation method based on a MoE system architecture. The super-large-scale multi-language neural machine translation method is an innovative and challenging technology. By reasonably designing and optimizing a model structure, a gating mechanism and a training strategy, the performance and efficiency of multi-language machine translation can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural language processing, and in particular to a very large-scale multilingual neural machine translation method based on a MoE architecture. Background Art

[0002] With the acceleration of globalization, the demand for multilingual translation is growing, especially in the scenario of ultra-large-scale language pairs. Traditional machine translation methods face the following challenges: Huge consumption of computing resources: As the number of language pairs increases, the amount of data that the model needs to process increases dramatically, making the training process extremely computationally intensive and time-consuming. This not only increases operating costs, but may also limit the model's ability to quickly adapt to new language pairs.

[0003] Limited model scalability: Existing models are often designed to be optimized for a specific number of language pairs. When expanding to more language pairs, the entire model may need to be retrained, which is not only inefficient but may also introduce new training biases.

[0004] Inconsistent translation quality: The grammatical and semantic differences between different language pairs are huge, resulting in uneven translation quality of the model on different language pairs. Especially in resource-scarce language pairs or low-resource scenarios, the translation effect is often difficult to guarantee.

[0005] Parameter redundancy and insufficient knowledge sharing: In multilingual models, there may be a large amount of shared language structure and knowledge between language pairs, but existing models often fail to fully utilize these commonalities, resulting in parameter redundancy and low model efficiency.

[0006] In recent years, Transformer-based neural network models have made significant progress in the field of machine translation, but their efficiency and performance still need to be improved when facing ultra-large-scale language pairs. Therefore, a more efficient, flexible and scalable multilingual neural machine translation method is needed. Summary of the invention

[0008] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a method for ultra-large-scale multilingual neural machine translation based on the MoE architecture, which combines the advantages of the MoE architecture to improve the efficiency and performance of multilingual neural machine translation (NMT), especially for the translation tasks of ultra-large-scale language pairs, and solves the challenges faced by traditional machine translation methods such as large consumption of computing resources and uneven translation quality. In recent years, the Transformer-based neural network model has made significant progress in the field of machine translation, but its efficiency and performance still need to be improved when facing ultra-large-scale language pairs.

[0009] (II) Technical solution In order to achieve the above advantages by combining the MoE architecture, aiming to improve the efficiency and performance of multilingual neural machine translation (NMT), especially for the translation task of ultra-large-scale language pairs, the present invention provides the following technical solutions: a method for ultra-large-scale multilingual neural machine translation based on the MoE architecture, comprising the following steps: S1, encoder and decoder adopt Transformer-based architecture, and embed MoE layer in it; The S2,MoE layer contains multiple expert models optimized for specific language pairs or language features; S3. Design a dynamic gating mechanism to select the most appropriate expert model for prediction based on the input data characteristics; S4. Introduce a load balancing mechanism to ensure that all expert models are trained and used evenly; S5, adopt the combination strategy of data parallelism and MoE parallelism for model training; S6. Apply knowledge distillation and quantization techniques to compress and optimize the model.

[0010] Preferably, in step S1, the expert model in the MoE layer can be an independent neural network or a sub-network that shares some parameters.

[0011] Preferably, in step S3, the gating mechanism selection strategy combines the softmax function and other machine learning algorithms.

[0012] Preferably, in step S3, a user-defined expert model interface is provided, allowing the user to upload or adjust expert model parameters according to the requirements of a specific language pair or language characteristics to further optimize the translation performance. The above interface includes defining a standard expert model configuration interface, through which the user can upload a new expert model or modify the parameters of an existing model, and the system automatically adjusts the expert model configuration in the MoE layer according to the user input.

[0013] Preferably, in step S3, a refined control method of a dynamic gating mechanism is set up, which can perform more accurate expert model selection based on multiple features of the input data, such as language type, text length, and vocabulary complexity. By introducing more complex feature extractors and decision trees, neural networks and other algorithms, the input data is analyzed in multiple dimensions, thereby more accurately selecting the most suitable expert model for translation prediction.

[0014] Preferably, in step S4, the load balancing mechanism can perform adaptive load balancing to implement a resource allocation strategy, which can dynamically adjust resource allocation according to system load, expert model performance and real-time translation requirements, including monitoring the overall system load, the utilization and response time of each expert model, and the priority of real-time translation requests, and dynamically adjusting resource allocation through intelligent algorithms to ensure that high-quality translation performance can be maintained under high load conditions.

[0015] Preferably, in step S5, an automated process integrating data parallelism and MoE parallel training is set up, which can automatically complete full-cycle tasks such as data preprocessing, model training, verification, deployment and updating, and realize full-process automated management from raw data to final deployed model through integrated automated tool chain, including automatic data partitioning, parallel training task scheduling, model performance evaluation and automatic updating, etc., thereby improving the efficiency of model development and deployment.

[0016] Preferably, in step S6, a knowledge distillation and quantization technology is set to be implemented in stages. The method adopts different compression and optimization strategies at different stages of model training to achieve the best balance between performance and efficiency. A relatively loose knowledge distillation strategy is adopted in the early stage of model training to retain as much model information as possible; more stringent quantization technology is gradually introduced in the later stage of training to reduce model parameters and calculation amount. At the same time, the combination of compression and optimization strategies is flexibly adjusted according to the actual application scenarios and performance requirements of the model.

[0017] Preferably, in steps S1-S6, the MoE-based multilingual neural machine translation model is configured to run compatibly on a variety of computing platforms and devices, and by optimizing the model structure and algorithm implementation, it is ensured that the model can run efficiently on different operating systems, hardware platforms and mobile devices, providing consistent and high-quality translation services.

[0018] (III) Beneficial effects Compared with the prior art, the present invention provides a very large-scale multilingual neural machine translation method based on the MoE architecture, which has the following beneficial effects: 1. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture, through the innovative fusion of encoder and decoder: This method first builds a powerful Transformer-based encoder-decoder framework as the basis for multilingual translation tasks. Under this framework, we innovatively embed MoE layers, which not only retain the advantages of the Transformer model in processing long-distance dependencies and complex syntactic structures, but also achieve flexible allocation and efficient utilization of computing resources by introducing the concept of expert models. This design enables the model to automatically adjust the allocation of computing resources when processing different language pairs to meet the needs of different language characteristics.

[0019] 2. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture is based on the refined design of expert models: Multiple expert models are integrated within the MoE layer, and each expert model is carefully designed and optimized to model specific language pairs or language features. These expert models can be completely independent neural networks or sub-networks that share some parameters, aiming to balance the complexity and generalization ability of the model. Through the refined design of the expert model, we ensure that the model can more accurately capture the subtle differences between languages ​​during the translation process, thereby improving the quality of translation.

[0020] 3. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture uses intelligent selection through a dynamic gating mechanism: In order to select the most suitable one for the current input data from multiple expert models for prediction, we designed an intelligent dynamic gating mechanism. This mechanism dynamically adjusts the weights of each expert model based on the characteristics of the input data (such as language ID, sentence structure, vocabulary distribution, etc.) through complex calculations and comparisons to achieve the selection of the optimal expert model. This dynamic selection process not only improves translation efficiency, but also ensures the accuracy and consistency of the translation results.

[0021] 4. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture is cleverly introduced through the load balancing mechanism: In order to ensure that all expert models can be fully utilized and trained during the training process, we introduced a load balancing mechanism. This mechanism monitors the load of each expert model and dynamically adjusts the input data allocation strategy to avoid the problem of some expert models being overloaded while other models are idle. At the same time, we also combine auxiliary loss functions or expert capacity balancing strategies to further optimize the load balancing effect and improve the overall performance of the model.

[0022] 5. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture uses an efficient training strategy: During the model training phase, we adopted a combination of data parallelism and MoE parallelism. By processing multiple data batches and expert models in parallel, we significantly improved the training speed and convergence efficiency of the model. This efficient training strategy not only shortens the model training cycle, but also reduces the demand for computing resources, making large-scale multilingual translation tasks more feasible and efficient.

[0023] 6. This ultra-large-scale multilingual neural machine translation method based on the MoE architecture uses advanced technologies for model compression and optimization: In order to further reduce the complexity and inference cost of the model, we applied knowledge distillation and quantization techniques. Knowledge distillation technology achieves model compression and acceleration by transferring the knowledge of a large teacher model to a small student model. Quantization technology further reduces the computational complexity and storage space requirements of the model by reducing the accuracy requirements of the model parameters. The application of these advanced technologies makes the model more economical and efficient in practical applications and meets the needs of different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 , the present invention provides the following technical solutions: A method for ultra-large-scale multilingual neural machine translation based on the MoE architecture includes the following steps: S1, encoder and decoder adopt Transformer-based architecture, and embed MoE layer in it; The expert model in the MoE layer can be an independent neural network or a sub-network that shares some parameters.

[0027] The S2,MoE layer contains multiple expert models optimized for specific language pairs or language features; S3. Design a dynamic gating mechanism to select the most appropriate expert model for prediction based on the input data characteristics; The gating mechanism selection strategy combines the softmax function and other machine learning algorithms.

[0028] A user-defined expert model interface is provided to allow users to upload or adjust expert model parameters according to the requirements of specific language pairs or language characteristics to further optimize translation performance. The above interface includes defining a standard expert model configuration interface, through which users can upload new expert models or modify the parameters of existing models. The system automatically adjusts the expert model configuration in the MoE layer according to user input.

[0029] A refined control method with a dynamic gating mechanism is set up. This method can make more accurate expert model selection based on multiple features of the input data, such as language type, text length, and vocabulary complexity. By introducing more complex feature extractors and decision trees, neural networks and other algorithms, a multi-dimensional analysis of the input data is performed, thereby more accurately selecting the most suitable expert model for translation prediction.

[0030] S4. Introduce a load balancing mechanism to ensure that all expert models are trained and used evenly; The load balancing mechanism can perform adaptive load balancing to implement a resource allocation strategy, which can dynamically adjust resource allocation based on system load, expert model performance, and real-time translation requirements. This includes monitoring the overall system load, the utilization and response time of each expert model, and the priority of real-time translation requests. Resource allocation is dynamically adjusted through intelligent algorithms to ensure high-quality translation performance under high load conditions.

[0031] S5, adopt the combination strategy of data parallelism and MoE parallelism for model training; Set up an automated process that integrates data parallelism and MoE parallel training. This process can automatically complete full-cycle tasks such as data preprocessing, model training, verification, deployment and update. By integrating the automated tool chain, it can achieve full automated management from raw data to the final deployed model, including automatic data partitioning, parallel training task scheduling, model performance evaluation and automatic update, etc., to improve the efficiency of model development and deployment.

[0032] S6. Apply knowledge distillation and quantization techniques to compress and optimize the model.

[0033] The knowledge distillation and quantization technology is implemented in stages. This method uses different compression and optimization strategies at different stages of model training to achieve the best balance between performance and efficiency. In the early stage of model training, a relatively loose knowledge distillation strategy is used to retain as much model information as possible; in the later stage of training, more stringent quantization technology is gradually introduced to reduce model parameters and calculations. At the same time, according to the actual application scenarios and performance requirements of the model, the combination of compression and optimization strategies is flexibly adjusted.

[0034] The MoE-based multilingual neural machine translation model is compatible with a variety of computing platforms and devices. By optimizing the model structure and algorithm implementation, it ensures that the model can run efficiently on different operating systems, hardware platforms and mobile devices, providing consistent and high-quality translation services.

[0035] This ultra-large-scale multilingual neural machine translation method based on the MoE architecture is achieved through the innovative fusion of encoders and decoders: This method first builds a powerful Transformer-based encoder-decoder framework as the basis for multilingual translation tasks. Under this framework, we innovatively embed MoE layers, which not only retain the advantages of the Transformer model in processing long-distance dependencies and complex syntactic structures, but also achieve flexible allocation and efficient utilization of computing resources by introducing the concept of expert models. This design enables the model to automatically adjust the allocation of computing resources when processing different language pairs to meet the needs of different language characteristics.

[0036] Through the refined design of expert models: Multiple expert models are integrated within the MoE layer, each of which is carefully designed and optimized to model specific language pairs or language features. These expert models can be completely independent neural networks or sub-networks that share some parameters, aiming to balance the complexity and generalization ability of the model. Through the refined design of expert models, we ensure that the model can more accurately capture the subtle differences between languages ​​during the translation process, thereby improving the quality of translation.

[0037] Intelligent selection through dynamic gating mechanism: In order to select the most suitable one for the current input data from multiple expert models for prediction, we designed an intelligent dynamic gating mechanism. Based on the characteristics of the input data (such as language ID, sentence structure, vocabulary distribution, etc.), this mechanism dynamically adjusts the weights of each expert model through complex calculations and comparisons to achieve the selection of the optimal expert model. This dynamic selection process not only improves translation efficiency, but also ensures the accuracy and consistency of the translation results.

[0038] Through the clever introduction of load balancing mechanism: In order to ensure that all expert models can be fully utilized and trained during the training process, we introduced a load balancing mechanism. This mechanism monitors the load of each expert model and dynamically adjusts the input data allocation strategy to avoid the problem of some expert models being overloaded while other models are idle. At the same time, we also combine auxiliary loss functions or expert capacity balancing strategies to further optimize the load balancing effect and improve the overall performance of the model.

[0039] Through efficient training strategies: During the model training phase, we adopted a combination of data parallelism and MoE parallelism. By processing multiple data batches and expert models in parallel, we significantly improved the training speed and convergence efficiency of the model. This efficient training strategy not only shortens the model training cycle, but also reduces the demand for computing resources, making large-scale multi-language translation tasks more feasible and efficient.

[0040] Advanced technologies for model compression and optimization: To further reduce the complexity and inference cost of the model, we applied knowledge distillation and quantization techniques. Knowledge distillation technology achieves model compression and acceleration by transferring the knowledge of a large teacher model to a small student model. Quantization technology further reduces the model's computational workload and storage space requirements by reducing the accuracy requirements of model parameters. The application of these advanced technologies makes the model more economical and efficient in practical applications, meeting the needs of different scenarios.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A very large-scale multilingual neural machine translation method based on the MoE architecture, characterized in that: The following steps are involved: S1, encoder and decoder adopt Transformer-based architecture, and embed MoE layer in it; The S2,MoE layer contains multiple expert models optimized for specific language pairs or language features; S3. Design a dynamic gating mechanism to select the most appropriate expert model for prediction based on the input data characteristics; S4. Introduce a load balancing mechanism to ensure that all expert models are trained and used evenly; S5, adopt the combination strategy of data parallelism and MoE parallelism for model training; S6. Apply knowledge distillation and quantization techniques to compress and optimize the model.

2. According to claim 1, a very large-scale multilingual neural machine translation method based on MoE architecture is characterized in that: In step S1, the expert model in the MoE layer can be an independent neural network or a sub-network that shares some parameters.

3. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S3, the gating mechanism selection strategy combines the softmax function and other machine learning algorithms.

4. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S3, a user-defined expert model interface is provided to allow the user to upload or adjust expert model parameters according to the requirements of a specific language pair or language characteristics to further optimize the translation performance. The interface includes defining a standard expert model configuration interface, through which the user can upload a new expert model or modify the parameters of an existing model. The system automatically adjusts the expert model configuration in the MoE layer according to the user input.

5. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S3, a refined control method of a dynamic gating mechanism is set up. This method can perform more accurate expert model selection based on multiple features of the input data, such as language type, text length, and vocabulary complexity. By introducing more complex feature extractors and decision trees, neural networks and other algorithms, the input data is analyzed in multiple dimensions, thereby more accurately selecting the most suitable expert model for translation prediction.

6. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S4, the load balancing mechanism can perform adaptive load balancing to implement a resource allocation strategy, which can dynamically adjust resource allocation according to system load, expert model performance and real-time translation requirements, including monitoring the overall system load, the utilization and response time of each expert model, and the priority of real-time translation requests, and dynamically adjusting resource allocation through intelligent algorithms to ensure that high-quality translation performance can be maintained under high load conditions.

7. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S5, an automated process integrating data parallelism and MoE parallel training is set up, which can automatically complete full-cycle tasks such as data preprocessing, model training, verification, deployment and update. By integrating the automated tool chain, full-process automated management from raw data to the final deployed model is achieved, including automatic data partitioning, parallel training task scheduling, model performance evaluation and automatic update, etc., thereby improving the efficiency of model development and deployment.

8. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In step S6, a knowledge distillation and quantization technology is set to be implemented in stages. The method adopts different compression and optimization strategies at different stages of model training to achieve the best balance between performance and efficiency. A relatively loose knowledge distillation strategy is adopted in the early stage of model training to retain as much model information as possible; more stringent quantization technology is gradually introduced in the later stage of training to reduce model parameters and calculation amount; At the same time, the compression and optimization strategy combination can be flexibly adjusted according to the actual application scenarios and performance requirements of the model.

9. The method for ultra-large-scale multilingual neural machine translation based on MoE architecture according to claim 1, characterized in that: In the steps S1-S6, the MoE-based multilingual neural machine translation model is configured to run compatibly on a variety of computing platforms and devices. By optimizing the model structure and algorithm implementation, it is ensured that the model can run efficiently on different operating systems, hardware platforms and mobile devices, providing consistent and high-quality translation services.

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