Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation
Abstract
In this work we present two extensions to
the well-known dynamic programming beam
search in phrase-based statistical machine
translation (SMT), aiming at increased effi-
ciency of decoding by minimizing the number
of language model computations and hypothesis expansions. Our results show that language
model based pre-sorting yields a small improvement in translation quality and a speedup
by a factor of 2. Two look-ahead methods are
shown to further increase translation speed by
a factor of 2 without changing the search space
and a factor of 4 with the side-effect of some
additional search errors. We compare our approach with Moses and observe the same performance, but a substantially better trade-off
between translation quality and speed. At a
speed of roughly 70 words per second, Moses
reaches 17.2% BLEU, whereas our approach
yields 20.0% with identical models.
the well-known dynamic programming beam
search in phrase-based statistical machine
translation (SMT), aiming at increased effi-
ciency of decoding by minimizing the number
of language model computations and hypothesis expansions. Our results show that language
model based pre-sorting yields a small improvement in translation quality and a speedup
by a factor of 2. Two look-ahead methods are
shown to further increase translation speed by
a factor of 2 without changing the search space
and a factor of 4 with the side-effect of some
additional search errors. We compare our approach with Moses and observe the same performance, but a substantially better trade-off
between translation quality and speed. At a
speed of roughly 70 words per second, Moses
reaches 17.2% BLEU, whereas our approach
yields 20.0% with identical models.
Research Areas
