Reem Bazzal, and Maha Atwi

Abstract In their text Machine Translation System and Human Translation with economic texts (2013), Reem Bazzal, and Maha Atwi intend to discuss the proper use of available online machine translation technologies in economic translation and provide an objective view of the suitability and reliability ofthese systems in translation. To illustrate more, practical texts have been submitted and they were translated by both Machine Translation and Human Translation. The evaluation ofthe work of such technologies and of human translation came based to four different aspects: semantics, syntax, morphology and comprehension and they concluded that although Machine translation results are not that reliable, they can help in producing a well understood text and help in finding some terms and expressions related to the economic field. This paper is set out to verify this belief. Commentary For a fair comparison of the performance of different online MT systems and Human Translation on economic texts, they needed appropriate texts that the MT systems can evaluate. The texts they chose Money Laundering is made up of 406 words in its English version and the other text Difference between Tax avoidance and Tax Evasion is made up of 243 words in its English version. They are therefore a reliable test set for examining the translation quality of economic texts by MT systems and human translation and to produce substantial statistical analysis for their performance in quantitative evaluation. After translating the two texts by a human translation and machine translation which is google translate, we concluded the following: Semantics: When it comes to semantics, the meaning or the interpretation of a word or sentence we noticed that there are differences and similarities between the human translation and the machine translation. For example, the human translation in the text money laundering translated the word: wire transfer as تحويلات برقية while the machine translated it as تحويلات سلكية . The word catch in human translation meant مسك while in machine translation it meant قبض. The phrase changing the money’s currency was translated in human translation to تغيير وضع العملة النقدية while in machine translation it was translated to تغيير عملة النقود . Despite the differences, machine translation and human translation had some words and expressions that hold the same meaning. For example, Global financial markets الاسواقالماليةالعالمية/ Money laundering غسيلالاموال/Bank-secrecy law قوانينالسريةالمصرفية/ Bank-reporting laws قوانينالابلاغالمصرفية. Morphology: Concerning the morphology, machine translation has a problem when it comes to translation related to Inflectional morphemes, the exact number, gender of the subject and the exact verb. When translating from Arabic to English, the agreement between grammatically linked items was often missing which led to some sentences that can be hardly understood. While human translation has proved its efficiency in giving the exact verb tense number, possession, and comparison. Source Sentence: The temporary closure of international banks because of the earthquake I Japan, also dented profits. Human translation: وتسبّبالزلزالالمدمرالذيضرباليابانبالإقفالالمؤقتللبنوكالدولية،الأمرالذيأدّىإلىانخفاضهذهالأرباح Machine translation: إغلاقمؤقتلبنوكدوليبسببالزلزالأنااليابان،تراجعأيضاالأرباح. Source Sentence: Investors remain cautiously optimistic. Machine translation: ولكنلايزالالمستثمرونمتفائلونبحذر . Human translation: إلاأنالمستثمرينبقيوامتفائلبنبحذر Syntax: Concerning the word order (syntax): the human translation is better than machine translation. The word combination in human translation is respected and well organized. It is more accurate and follows the grammatical rules: subject-verb and we can also notice the proper use of words such determiners, modifiers, and complements While in machine translation a majority of phrases started with the verb or the verb phrase instead of the noun, and the structure is most likely similar to the combination of words in the Arabic language, hence, the Arabic uses the : verb-subject-object combinational structure while in the English language, it is subject-verb-object- prepositional phrase (noun group, adjective, adverb ,prepositional, and possessive) so it’s obvious that google translate was unable to give good results for example: Comprehension: After comparing the two methods of translation we noticed that the target texts in the machine translation and despite the mistakes can be understood. But the use of grammar and punctuation is not correct and the sentences are sometimes lengthy and complex. Also, MT produces some sentences that are unfortunately far from the meaning given in the source text. As for human translation, the texts we well understood, the meaning was clearly stated in the target language and the use of grammar and punctuation is correct. Take the following sentences as examples: Source Sentence: The rise of global financial markets makes money laundering easier than ever. Machine Translation : صعودالأسواقالماليةالعالميةيجعلغسلالأموالأسهلمنأيوقتمضى Human Translation : ارتفاعالاسواقالعالميةيجعلغسلالاموالاسهلبكثير Source Sentence: At the integration stage, the money re-enters the mainstream economy in legitimate-looking form. Machine Translation: فيمرحلةالتكامل،وإعادةالأمواليدخل-التيارالرئيسيللاقتصادفيشكلالمشروعةالمظهر Human Translation: فيمرحلةالتكامل, يتماعادةادخالالاموالللتيارالرئيسيللاقتصادفيشكلشرعي. Recommendations: It is important to understand that machine translation is an interesting field of research but it is not a substitute for a professional translation produced by a human translator who is able to give exact meanings of the terms and expressions used. It is necessary to develop the machine translation that are related to specific domains so that terms related to economic or legal can be easily conducted and used. It is preferable to add translation memories to all machine translation to make sure that terms used are saved for the purpose of using it in the translation of other texts that are related to the same domain. It is necessary to improve the ability of the machine translation in focusing more on the production of perfect economic texts not only on the comprehension side. It is important to develop new machine translation methods that can help in creating well grammar links and understood sentences.