A Probabilistic Model for Sign Language Translation Memory
In this paper, we present an approach for building translation memory for American Sign Language (ASL) from parallel corpora between English and ASL, by identifying new alignment combinations of words from existing texts. Our primary contribution is the application of several models of alignments for Sign Language. The model represents probabilistic relationships between properties of words, and relates them to learned underlying causes of structural variability within the domain. We developed a statistical machine translation based on generated translation memory. The model was evaluated on a big parallel corpus containing more than 800 millions of words. IBM Models have been applied to align Sign Language Corpora then we have run experimentation on a big collection of paired data between English and American Sign Language. The result is useful to build a Statistical Machine Language or any related field.
KeywordsSign Language Translation Memory Probabilistic Model
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