Source Language Adaptation Approaches for Resource-Poor Machine Translation
نویسندگان
چکیده
منابع مشابه
Source Language Adaptation Approaches for Resource-Poor Machine Translation
Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language. Thus, we propose three novel, language-independent approaches to source language adaptation for resource-poor statistical machine translation. Specifically, we build improved statistical machine translation models from a resource-poor langua...
متن کاملSource Language Adaptation for Resource-Poor Machine Translation
We propose a novel, language-independent approach for improving machine translation from a resource-poor language to X by adapting a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resourcepoor language to X pair, which we use to learn word-level and phrase-level paraphrases and cross-lingual morphological variants between t...
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We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some reso...
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The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With diffe...
متن کاملImproved Statistical Machine Translation for Resource-Poor Languages Using Related Resource-Rich Languages
We propose a novel language-independent approach for improving statistical machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resourcepoor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y fo...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2016
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli_a_00248