ATLASLang NMT: Arabic text language into Arabic sign language neural machine translation
| Autor: | Mourad Brour, Abderrahim Benabbou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Interlingua
General Computer Science Machine translation Exploit Arabic Computer science 02 engineering and technology computer.software_genre Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering Arabic sign language Machine translation system Artificial neural network business.industry Hearing impaired 020206 networking & telecommunications QA75.5-76.95 language.human_language Natural language processing Arabic language Electronic computers. Computer science language 020201 artificial intelligence & image processing Artificial intelligence business computer Deaf people Natural language processing Word (computer architecture) |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 33, Iss 9, Pp 1121-1131 (2021) |
| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2019.07.006 |
| Popis: | ATLASLang is a machine translation system from Arabic text language into Arabic sign language (ArSL). The first version of the system (Brour and Benabbou, 2019) is based on two approaches: rule-based Interlingua and example-based approaches. It is a classical machine translation system, its limitations are the linguistic knowledge necessary to develop the rules, also many rules and exceptions required. In the last few years, a neural machine translation has achieved notable results, and several well-known companies including Google (Wu et al., 2016) and Systran (Crego et al., 2016) have starting to exploit it. In this paper, we present a new version of ATLASLang that is implemented using a feed-forward back-propagation Artificial Neural Network. In this version, we have started to translate simple sentences composed from a limited number of words since generally communication with deaf person uses short sentences. We have used the same ATLASLang MTS database of signs and we have utilized morphological characteristics to derive the maximum information from each word in the input of the neural translation system. The two versions of the system are compared using n-gram BLEU score (Papineni et al., 2002), and the results demonstrate that neuron network approaches outperform the other classical approaches. |
| Databáze: | OpenAIRE |
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