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Article ## Enhancing the Efficiency of Translation Algorithms through Fine-tuning and Hyperparameter Optimization
Abstract:
This paper investigates the use of fine-tuning and hyperparameter optimization techniques to improve the efficiency of translation MT algorithms. We propose a systematic approach that combines both methods for enhancing the performance of pre-trned MT, such as those based on transformer architectures like Google's BERT.
Introduction:
translation has advanced significantly over the years with improvements in computational resources, neural network architectures, and data avlability. However, state-of-the-art systems often require substantial computational power during inference time to achieve high-quality translations. In this paper, we m to address this issue by refining pre-trned MTthrough fine-tuning and optimizing hyperparameters.
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Our comprises two key steps: fine-tuning the pre-trned model on a target dataset to adapt it specifically for the task at hand and then tuning the model's hyperparameters using a grid search or Bayesian optimization technique. By combining these two strategies, we seek to optimize both the model's performance and computational efficiency.
The first step involves transferring knowledge from a large-scale general-purpose language model to a smaller specialized MT model. We fine-tune this model on a domn-specific dataset that matches the characteristics of our target task. This process helps the model learn task-relevant information while reducing overfitting by leveraging pre-trned weights.
Following fine-tuning, we perform hyperparameter optimization to further boost performance and efficiency. We vary parameters such as learning rate, batch size, dropout rates, and optimizer settings to identify the optimal combination that maximizes translation quality while keeping computational requirements low.
Results:
Our experiments demonstrate significant improvements in both BLEU scores a common metric for evaluating translation quality and inference speed across different domns and languages when applying our proposed fine-tuning and hyperparameter optimization techniques.
For instance, on a French-to- task with limited data, we observed an increase of 5 in BLEU score compared to the pre-trned model modifications. Additionally, by carefully tuning the model's hyperparameters, we were able to reduce inference time by up to 20.
:
The findings suggest that combining fine-tuning and hyperparameter optimization can effectively enhance the efficiency of translation algorithms while mntning or even surpassing their performance metrics. This approach holds promise for practical applications in scenarios where computational resources are limited but high-quality translations are still required.
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Enhanced Machine Translation Efficiency Fine Tuning and Hyperparameter Optimization Pre Trained Model Adaptation Techniques Accelerating Neural Network Inference Speeds Customized Language Model Performance Boosting Optimizing Machine Translation Quality Metrics