{"id":34,"date":"2017-01-19T12:22:40","date_gmt":"2017-01-19T12:22:40","guid":{"rendered":"http:\/\/achrafothman.net\/site\/?page_id=34"},"modified":"2021-04-15T03:18:21","modified_gmt":"2021-04-15T03:18:21","slug":"asl-smt","status":"publish","type":"page","link":"https:\/\/achrafothman.net\/site\/asl-smt\/","title":{"rendered":"Statistical Machine Translation for Sign Language (ASL-SMT)"},"content":{"rendered":"

ASL-SML deal with machine translation to sign language. It starts with studying existing systems and issues in order to propose a new model for statistical machine translation from written English text to American Sign Language (English\/ASL). The study covers specificity of Sign Language from different communities and a scope of existing tools and solutions. According to the state of the art, the aims of this paper is to propose a new approach aiming to build artificial corpus using grammatical dependencies rules due to the lack of resources for Sign Language. The parallel corpus was the input of our machine translation that has been used to create the statistical memory translation based on the IBM alignment algorithms. These algorithms have been improved and optimized by integrating Jaro-Winkler distances. Then, based on the constructed translation memory, we have implemented a decoder to translate an English text to the American Sign Language using a new transcription system based on gloss annotation. Results had been evaluated by the BLEU evaluation metric.<\/p>\n

if you are interested to deploy your own machine translation, check my tutorial here<\/a> or in my youtube channel (TechCarrot)<\/a>.<\/p><\/blockquote>\n

Link to the live demo<\/h3>\n
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Corpus Sample Data:<\/strong><\/h3>\n