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his article shows that the structure of bilingual material from standard parsing and alignment tools is not optimal for training syntax-based statistical machine translation (SMT) systems. We presen...
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 describe a systematic analysis on the effectiveness of features commonly exploited for the problem of predicting machine translation quality. Using a feature selection technique based on Gaussian P...
Most modern machine translation systems use phrase pairs as translation units, allowing for accurate modelling of phraseinternal translation and reordering. However phrase-based approaches are much le...
Modern phrase-based machine translation systems make extensive use of wordbased translation models for inducing alignments from parallel corpora. This is problematic, as the systems are incapable of a...
Annotating linguistic data is often a complex, time consuming and expensive endeavour. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bringing different b...
We describe QUEST, an open source framework for machine translation quality estimation. The framework allows the extraction of several quality indicators from source segments, their translations, exte...
We introduce synchronous tree adjoining grammars (TAG) into tree-to-string transla-tion, which converts a source tree to a target string. Without reconstructing TAG deriva-tions explicitly, our rule e...
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...
Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar).Though this practice improves translation by weakening independence a...
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...
As tokenization is usually ambiguous for many natural languages such as Chinese and Korean, tokenization errors might po-tentially introduce translation mistakes for translation systems that rely on 1...
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...
Tree-to-string systems (and their forest-based extensions) have gained steady pop-ularity thanks to their simplicity and effi-ciency, but there is a major limitation: they are unable to guarantee the ...
Syntax-based translation models should in principlebe efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integr...

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