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搜索结果: 1-11 共查到计算机科学技术 Statistical Machine Translation相关记录11条 . 查询时间(0.093 秒)
In this paper, we report on a set of initial results for English-to-Arabic Statistical Machine Translation (SMT). We show that morphological decomposition of the Arabic source is beneficial, especiall...
We propose Maximum Ranking Correlation (MRC) as an objective function in discrimi-native tuning of parameters in a linear model of Statistical Machine Translation (SMT). We try to maximize the ranking...
Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on dependency structure based models typi-cally resort to ins...
In this paper, we propose a novel dependency-based bracketing transduc-tion grammar for statistical machine translation, which converts a source sen-tence into a target dependency tree. Dif-ferent fro...
This paper illustrates the ICT Statistical Machine Transla-tion system used in the evaluation campaign of the Interna-tional Workshop on Spoken Language Translation 2010. We participate in the DIALOG ...
We propose a structure calleddependency forest for statistical machine translation.A dependency forest compactly represents multiple dependency trees. We develop new algorithms for extracting string-t...
In this paper, we propose a nove dependency-based bracketing transduc-tion grammar for statistical machine translation, which converts a source sen-tence into a target dependency tree. Dif-ferent from...
We describe a novel approach for syntaxbased statistical MT, which builds on a variant of tree adjoining grammar (TAG). Inspired by work in discriminative dependency parsing, the key idea in our appro...
We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large number of new features to two machine translation systems: the Hiero hierarchical phrasebased translation system and our sy...
Current statistical machine translation sys-tems usually extract rules from bilingual corpora annotated with 1-best alignments.They are prone to learn noisy rules due to alignment mistakes. We propose...

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