Fast machine translation on parallel and massively parallel hardware
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Date
01/07/2019Author
Bogoychev, Nikolay Veselinov
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Abstract
Parallel systems have been widely adopted in the field of machine translation, because
the raw computational power they offer is well suited to this computationally intensive
task. However programming for parallel hardware is not trivial as it requires redesign
of the existing algorithms. In my thesis I design efficient algorithms for machine translation
on parallel hardware. I identify memory accesses as the biggest bottleneck to
processing speed and propose novel algorithms that minimize them. I present three distinct
case studies in which minimizing memory access substantially improves speed:
Starting with statistical machine translation, I design a phrase table that makes decoding
ten times faster on a multi-threaded CPU. Next, I design a GPU-based n-gram
language model that is twice as fast per £ as a highly optimized CPU implementation.
Turning to neural machine translation, I design new stochastic gradient descent techniques
that make end-to-end training twice as fast. The work in this thesis has been
incorporated in two popular machine translation toolkits: Moses and Marian.