Pipeline-tolerant decoder
نویسنده
چکیده
This work presents a modified version of the cube pruning decoding algorithm used for machine translation. Our pipeline-tolerant decoding algorithm allows multiple translation hypotheses to be scored at the same time. We demonstrate the benefits of scoring multiple hypotheses simultaneously using a pipeline for language model scoring. Our pipeline builds on top of previous work on memory prefetching and improves the speed of computing language model scores. We implemented our pipeline-tolerant decoder and the language model pipeline inside Moses and our benchmarks showed improvements in translation speed.
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