Multi-Lingual Phrase-Based Statistical Machine Translation for Arabic-English
Ahmed Bastawisy, Mohamed Elmahdy · 2017
In this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation.Arabic Text can be categorized into standard and dialectal Arabic.These two forms of Arabic differ significantly.Different mono-lingual and multi-lingual hybrid SMT approaches are compared.Mono-lingual systems do always result in better translation accuracy in one Arabic form and poor accuracy in the other.Multi-lingual SMT models that are trained with pooled parallel MSA/dialectal data result in better accuracy.However, since the available parallel MSA data are much larger compared to dialectal data, multilingual models are biased to MSA.We propose in the work, a multi-lingual combination of different mono-lingual systems using an Arabic form classifier.The outcome of the classier directs the system to use the appropriate mono-lingual models (standard, dialectal, or mixture).Testing the different SMT systems shows that the proposed classifier-based SMT system outperforms mono-lingual and datapooled multi-lingual systems.