Distributed Machine Translation Training System
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Solution Overview
Problem
Conventional machine translation training methods are inefficient due to the need for multiple training runs on large datasets, leading to prolonged processing times and resource bottlenecks when performed on single machines, which affects the speed and accuracy of translation processes.
Innovation Solution
A distributed machine translation training system utilizing a task distribution server, mapping servers, and reduction servers, employing the MapReduce programming model to parallelize tasks such as word-alignment, phrase-extraction, and language modeling, which reduces resource usage and training time by distributing data and computations across multiple machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation training is performed on a single standalone machine, then the system structure is simple, but the training time becomes excessively long and processing efficiency is low
Solution Approach 1:
The patent divides the training corpus into multiple partitions and distributes them across multiple machines. Each machine processes a specific partition independently, performing training tasks on segmented data. This segmentation enables parallel processing and significantly reduces overall training time while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent combines multiple independent training processes running on different machines into a unified training system. Results from all machines are aggregated and integrated to produce the final trained model. This merging approach maintains simplicity at the individual machine level while achieving high productivity through coordinated parallel processing.
2Manufacturing precision
If multiple training runs are performed on large training corpora, then translation accuracy improves, but the processing time increases significantly
Solution Approach 1:
The training corpus is divided into multiple partitions that can be processed simultaneously across different machines. Each partition undergoes the necessary training runs for accuracy improvement, but these processes occur in parallel rather than sequentially. This segmentation maintains high translation accuracy while reducing total processing time through concurrent execution.
Solution Approach 2:
The system maintains continuous training operations across multiple machines simultaneously, eliminating idle time between training runs. While one machine completes its training cycle on a partition, another machine is already processing the next partition. This continuous parallel action ensures accuracy requirements are met without the time loss associated with sequential processing.
3Speed
If training results are loaded into a single machine's memory, then data access is centralized, but query speed decreases due to the massive amount of data
Solution Approach 1:
The training results are divided into multiple segments and distributed across the memory spaces of different machines in the cluster. Each machine stores a portion of the results, allowing queries to be processed in parallel across multiple nodes. This segmentation dramatically improves query speed by distributing the data access load while managing complexity through organized data partitioning.
Data Source
AI summary
A machine translation training system is provided. The system includes a task distribution server, a plurality of mapping servers, and a plurality of reduction servers. During operation, the task distribution server is configured to distribute a first translation training task and a training corpus to the mapping servers and distribute a second translation training task and first translation training results received from the mapping servers to the reduction servers. A respective mapping server is configured to receive a portion of the training corpus and perform the first translation training task on the received portion of the training corpus. A respective reduction server is configured to receive a subset of the first translation training results that correspond to a same language element, perform the second translation training task on the received subset of the first translation training results, and output second translation training results.