Hybrid Arabic-English Machine Translation to Solve Reordering and Ambiguity Problems - Alubaidi, Khalid Shaker (Journal of University of Human Development, 2015)
The problem in Arabic to English rule-based machine translation is that the rule-based lexical analyzer leaves some amount of ambiguity; therefore a statistical approach is used to resolve the ambiguity problem. Rule Based Machine Translation (RBMT) uses linguistic rule between two languages which is built manually by human in general, whereas SMT uses appearance statistic of word in parallel corpora. In this paper, those different approaches are combined into Arabic-English Hybrid Machine Translation (HMT) system to get the advantage from both kind of information. In the beginning, Arabic text will be inputted into RBMT to solve reordering problem. Then, the output will be edited by SMT to solve the ambiguity problem and generate the final translation of English text. SMT is capable to do this because on the training process, it uses RBMT’s output (English) as source material and real translation (English) as target material. The results showed that the quality of translation in HMT system is better than SMT system.