Statistical Machine Translation for Greek to Greek Sign Language Using Parallel Corpora Produced via Rule-Based Machine Translation

One of the objectives of Assistive Technologies is to help people with disabilities communicate with others and provide means of access to information. As an aid to Deaf people, we present in this work a novel prototype Rule-Based Machine Translation (RBMT) system for the creation of large quality written Greek text to Greek Sign Language (GSL) glossed corpora. In particular, the proposed RBMT system supports the professional translator of GSL to produce high quality parallel Greek text GSL glossed corpus, which is then used as training data by the Statistical Machine Translation (SMT) MOSES [1] application system. It should be noted that the whole process is robust and flexible, since it does not demand deep grammar knowledge of GSL. With this work we manage to overcome the two biggest obstacles in Natural Processing Language (NLP) of GSL. Firstly, the lack of written system and secondly the lack of grammar and finally we have been able to lay the foundations for an autonomous translation system of Greek text to GSL. Evaluation of the proposed scheme is carried out in the weather reports domain, where 20,284 tokens and 1,000 sentences have been produced. By using the BiLingual Evaluation Understudy (BLEU) metric score, our prototyped MT system achieves a relative average score of 60.53% and 85.1%/65.5%/53.8%/44.8% for for 1-gram/2gram/3-gram/4-gram evaluation.