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Preprint · arXiv · November 9, 2016

Increasing the throughput of machine translation systems using clouds

Jernej Vičič, Andrej Brodnik

Jernej Vičič
Read on arXivPDFDOI 10.48550/arxiv.1611.02944

Abstract

The manuscript presents an experiment at implementation of a Machine Translation system in a MapReduce model. The empirical evaluation was done using fully implemented translation systems embedded into the MapReduce programming model. Two machine translation paradigms were studied: shallow transfer Rule Based Machine Translation and Statistical Machine Translation. The results show that the MapReduce model can be successfully used to increase the throughput of a machine translation system. Furthermore this method enhances the throughput of a machine translation system without decreasing the quality of the translation output. Thus, the present manuscript also represents a contribution to the seminal work in natural language processing, specifically Machine Translation. It first points toward the importance of the definition of the metric of throughput of translation system and, second, the applicability of the machine translation task to the MapReduce paradigm.

Natural Language Processing TechniquesCloud Computing and Resource ManagementParallel Computing and Optimization Techniquesmachine translationMapReducecloud computing

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