Hybrid Machine Translation by Combining Output from Multiple Machine Translation Systems
نویسندگان
چکیده
منابع مشابه
Combining Outputs from Multiple Machine Translation Systems
Currently there are several approaches to machine translation (MT) based on different paradigms; e.g., phrasal, hierarchical and syntax-based. These three approaches yield similar translation accuracy despite using fairly different levels of linguistic knowledge. The availability of such a variety of systems has led to a growing interest toward finding better translations by combining outputs f...
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The Carnegie Mellon multi-engine machine translation software merges output from several machine translation systems into a single improved translation. This improvement is significant: in the recent NIST MT09 evaluation, the combined Arabic-English output scored 5.22 BLEU points higher than the best individual system. Concurrent with this paper, we release the source code behind this result co...
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In this paper, we address the problem of computing a consensus translation given the outputs from a set of Machine Translation (MT) systems. The translations from the MT systems are aligned with a multiple string alignment algorithm and the consensus translation is then computed. We describe the multiple string alignment algorithm and the consensus MT hypothesis computation. We report on the su...
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Word alignment is a critical component of machine translation systems. Various methods for word alignment have been proposed, and different models can produce significantly different outputs. To exploit the advantages of different models, we propose three ways to combine multiple alignments for machine translation: (1) alignment selection, a novel method to select an alignment with the least ex...
متن کاملCombining Machine Translation Output with Open SourceThe Carnegie Mellon Multi-Engine Machine Translation Scheme
The Carnegie Mellon multi-engine machine translation software merges output from several machine translation systems into a single improved translation. This improvement is significant: in the recent NIST MT09 evaluation, the combined Arabic-English output scored 5.22 BLEU points higher than the best individual system. Concurrent with this paper, we release the source code behind this result co...
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ژورنال
عنوان ژورنال: Baltic Journal of Modern Computing
سال: 2019
ISSN: 2255-8950
DOI: 10.22364/bjmc.2019.7.3.01