Increasing the throughput of machine translation systems using clouds
This work addresses throughput issues for machine translation users, but it is incremental as it applies an existing MapReduce paradigm to known translation methods.
The authors tackled the problem of low throughput in machine translation systems by implementing them in a MapReduce model, resulting in increased throughput without compromising translation quality.
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.