A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation. (September 2018) - Kouremenos, Dimitrios; Ntalianis, Klimis; Kollias, Stefanos
Highlights: The system produces large amounts of written GSL glossed corpora of high quality. The glossed corpus is used as training data for creating Language Model n-grams. Language Models for written GSL gloss are missing from the literature. The system achieves a relative score of 84% for 4-grams, which is very promising. The system accelerates the translation process, carried out by professionals. Abstract: One of the aims of assistive technologies is to help people with disabilities to communicate with others and to provide means of access to information. As an aid to Deaf people, in this work we present a novel prototype Rule Based Machine Translation (RBMT) system for the creation of large and quality written Greek Sign Language (GSL) glossed corpora from Greek text. In particular, the proposed RBMT system assists the professional GSL translator in speeding up the production of different kinds of GSL glossed corpora. Then each glossed corpus is used for the production/creation of Language Model (LM) n-grams. With the GSL glossed corpus from Greek text, we can build, test and evaluate different kinds of Language Models for different kinds of glossed GSL corpora. Here, it should be noted that it does not require grammar knowledge of GSL but only very basic GSL phenomena covered by manual RBMT rules as it assists the professional human translator. Furthermore, it should also be stressed that Language Models for written GSL gloss are missing from the scientific literature, thus this work is pioneer in this field. Evaluation of the proposed scheme is carried out for the weather reports domain, where 20, 284 tokens and 1000 sentences have been produced. By using the BiLingual Evaluation Understudy (BLEU) metric score, our prototype RBMT system achieves a relative score of 0.84 (84%) for 4-grams and 0.9 (90%) for 1-grams.