A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)
This work addresses translation accuracy for English-Arabic pairs, but it is incremental as it builds on existing SMT methods.
The authors tackled the problem of improving statistical machine translation from English to Arabic by combining multiple statistical and NLP models, and reported that their hybrid model enhanced translation results compared to systems like Google Translate.
The interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a sentence from the source Language (English) to the target language (Arabic) automatically through efficiently incorporating different statistical and Natural Language Processing (NLP) models such as language model, alignment model, phrase based model, reordering model, and translation model. These models are combined to enhance the performance of statistical machine translation (SMT). Many implementation tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and BLEU. Based on the implementation, evaluation of this model, and comparing the generated translation with other implemented machine translation systems like Google Translate, it was proved that this proposed model has enhanced the results of the statistical machine translation, and forms a reliable and efficient model in this field of research.