Multi-Engine Machine Translation with an Open-Source SMT Decoder
Proceedings of the Second Workshop on Statistical Machine Translation |
Published by Association for Computational Linguistics
We describe an architecture that allows
to combine statistical machine translation
(SMT) with rule-based machine translation
(RBMT) in a multi-engine setup. We use a
variant of standard SMT technology to align
translations from one or more RBMT systems
with the source text. We incorporate
phrases extracted from these alignments into
the phrase table of the SMT system and use
the open-source decoder Moses to find good
combinations of phrases from SMT training
data with the phrases derived from RBMT.
First experiments based on this hybrid architecture
achieve promising results.