Based on an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup, we present new results that show that this type of system combination can actually increase the lexical coverage of the resulting hybrid system, at least as far as this can be measured via BLEU score.
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.
We present a simple method for generating translations with the Moses toolkit (Koehn et al., 2007) from existing hypotheses produced by other translation engines. As the structures underlying these translation engines are not known, an evaluationbased strategy is applied to select systems for combination. The experiments show promising improvements in terms of BLEU.
We present a word substitution approach to combine the output of different machine translation systems. Using part of speech information, candidate words are determined among possible translation options, which in turn are estimated through a precomputed word alignment. Automatic substitution is guided by several decision factors, including part of speech, local context, and language model probabilities. The combination of these factors is defined after careful manual analysis of their respective impact. The approach is tested for the language pair GermanEnglish, however the general technique itself is language independent.
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