Massively Parallel Suffix Array Queries and On-Demand Phrase Extraction for Statistical Machine Translation Using GPUs

Hua He, Jimmy Lin and Adam Lopez

Translation models in statistical machine translation can be scaled to large corpora and arbitrarily-long phrases by looking up translations of source phrases "on the fly" in an indexed parallel corpus using suffix arrays. However, this can be slow because on-demand extraction of phrase tables is computationally expensive. We address this problem by developing novel algorithms for general purpose graphics processing units (GPUs), which enable suffix array queries for phrase lookup and phrase extraction to be massively parallelized. Compared to a highly-optimized, state-of-the-art serial CPU-based implementation, our techniques achieve at least an order of magnitude improvement in terms of throughput. This work demonstrates the promise of massively parallel architectures and the potential of GPUs for tackling computationally-demanding problems in statistical machine translation and language processing.

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