Large Scale Machine Translation Language Model Training System Based on Spark

When faced with large language datasets, machine translation language models are difficult to process, resulting in low accuracy and efficiency of translation. Therefore, this study constructs a framework based on Firstly, by installing Apache Spark and configuring its integration with Hadoop Distributed File System (HDFS), a distributed computing environment capable of parallel data processing is established to support rapid processing of large-scale data. Then, the distributed storage system HDFS is adopted to optimize data access efficiency and reduce IO (Input/Output) bottlenecks. Finally, a new iterative training strategy is implemented to gradually improve the translation accuracy of the model through incremental learning. Under the optimal configuration, the combination of a learning rate of 0.1 and batch processing of 2048 achieves a BLEU score of up to 30.8%, with training lasting only 4 hours. This study demonstrates the effectiveness of a large-scale machine translation language model training system based on Spark in improving translation efficiency and accuracy.