Computer-Assisted Translation: December 2013

Tuesday, December 17, 2013

Reem Bazzal, and Nour Onies

Abstract In their text Evaluation of Machine Translation System and Human Translation with Literary texts (2013), Reem Bazzal, and Nour Onies intend to discuss the proper use of available online machine translation technologies in literary translation and provide an objective view of the suitability and reliability of these systems in translation. To illustrate more, practical text has been submitted and it was translated by both Machine Translation and Human Translation. They evaluated the work of such technologies and of human translation from four different aspects: semantics, syntax, morphology and comprehension and concluded that although Machine translation results are not that reliable, they can help in producing a well understood text. This paper is set out to verify this belief. Comprehension For a fair comparison of the performance of different online MT systems and Human Translation on literary texts, they needed appropriate text that the MT systems can evaluate. The text they choseThe Raven by Edgar Allan Poe is made up of 406 words in its English version. It is therefore a reliable test set for examining the translation quality of literary texts by MT systems and human translation and to produce substantial statistical analysis for their performance in quantitative evaluation. After translating the text by both 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, in human translation, the expression midnight dreary was translated into ليلةٍ مقفرة while google translate translated it into ليلة كئيبة. Another example is the word pondered which in human translation meant تأملت while in machine translation it was translated to فكرت. “Rapping at my chamber door" was translated into “يقرع علىبابمخدعي" in human translation while with google translate it was translated to "موسيقىالرابفيبابحجرتي 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. For example: Source sentence: And the silken sad uncertain rustling of each purple curtain Thrilled me Machine translation: وحريريسرقةمؤكدحزينةلكلستارارجواني Human translation: الحفيفُالحريريُّالحزينُوالغامضُلكلِّستارةٍأرجوانية أثارني Source sentence: While I nodded, nearly napping, suddenly there came a tapping Machine translation: بينماكنتضربةرأس،القيلولةتقريبا،فجأةهناكجاءالتنصت، Human translation: وعندماسقطرأسيعلىصدريوأوشكتُأنأغفو فجأةًجاءَالطرقُ 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 English to Arabic, 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. For example: Source text: While I nodded, nearly napping, suddenly there came a tapping Human translation: وعندماسقطرأسيعلىصدريوأوشكتُأنأغفو Machine translation: بينماكنتضربةرأس،القيلولةتقريبا،فجأةهناكجاءالتنصت، Another example on the wrong morphological interpretation that was clearly stated in the translation process: Source text: As of someone gently rapping, rapping at my chamber door. Human translation: وكأنأحداًبرفقٍيقرعُ...يقرعُعلىبابِمخدعي Machine translation: اعتبارامنبعضواحدموسيقىالراببلطف،موسيقىالرابفيبابحجرتي 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: Once upon a midnight dreary Machine Translation:مرةواحدةعندمنتصفالليلالكئيب Human Translation:فيليلةٍ مقفرةٍعندمنتصفالليل Source sentence: Over many a quaint and curious volume of forgotten lore Machine Translation:أكثرمنوحدةتخزينالعديدغريبةوغريبةمنالعلمالنسيان Human Translation مررتُبعديدٍ منالمعتقداتِالقديمةِالغريبة التيغطاهاألنسيان Source sentence: distinctly I remember it was in the bleak December Machine Translation:بوضوحتذكرت،أنهكانفيديسمبرالقابض Human Translation:أتذكّرُبوضوحٍ إنه ديسمبرالكثيب Source sentence: the rare and radiant maiden whom the angels name Lenore Machine Translation: النادرةالمتألقةالنادرةالذينلينوراسمالملائكة Human translation: لتلكالفتاةِ الرائعةِالمتألقة التيأسمتهاالملائكةُُلينور Recommendations It is important to include in MT engines special lexicon domains that can be used in literal translation, which offers quite a wide variety of lexicons if one accesses the MT sites directly. It is necessary to take into consideration that on-line MT engines are aimed at helping users deal with ephemeral literary texts and they help communication in many situations. 1. It is important to understand that machine translation has definitely improved the work of journalistic translation whether by the use of blogs, emails or portables like new Samsung phones (S. Translator) and IPhone phones( I translate) and the intelligent use of them led to the focus of the best human efforts where they should be. Machine translation like blogs and iPad and S. translator must be used an alternative to the expensive human translation that takes time and is usually unavailable when it is needed for communicating quickly and cheaply with people with whom we do not share a common language.

Ghina Khamis and Nour Al Oneis

Evaluation of Machine Translation Tools and Human Translation in Economic Translation Ghina Khamis and Nour Al Oneis Instructor: Dr Ghada Awada Computer-Assisted Translation TRNS301 Table of Contents : Abstract……………………………………………………………1 1- Commentary………………………………………….….....2 1.1 -Test Texts……………………………………….……..2 1.2-Evaluation……………………………………………....2 1.2.1- Semantics……………………………………..2 1.2.2- Morphology……………………………….….3 1.2.3- Syntax………………………………………...4 1.2.4- Pragmatics…………………………………....5 1.2.5- Comprehension…………………………….....6 2-Recommendations…………………………………………...7 3-Conclusion……………………………………………….......8 Terminology………………………………………………………9 References………………………………………………………..13 Abstract : In their evaluation of Human Translation and Machine Translation, Nour Al Oneis and Ghina Khamis (2013) studied and compared the differences between human’s and machine’s translation, defining the nature of translation and standards and criteria on which it is based, studying semantics, syntax, pragmatics, morphology and comprehension. This study aims to compare the effectiveness of the popular machine translation system (Google Translate) used to translate English sentences into Arabic relative to the effectiveness of English to Arabic human translation. This study will show that the Human Translation is better than Google machine translation system in terms of precision of translation from English to Arabic. This paper is set out to study the accuracy of this believe. 1-Commentary: 1.1 Test texts We performed the evaluation on two economical texts of the writer Alan Blanchette, the first entitled “Tax Avoidance” and the second “Money Laundering” , both published in 2010 and which has been reserved for testing. The texts have 504-636 words. All examples from the two texts are illustrated in that evaluation. 1.2- Evaluation: 1.2.1- Semantics: Semantic roles can determine how to properly express information in another language. Regarding the study of the meaning of linguistic expressions, it’s noteworthy to mention that many expressions and words differ in meaning between the human translation and the machine translation. Example: English العربية Human Machine catch مسك قبض Involves تشمل تتضمن wire transfers تحويلات برقية تحويلت سلكية changing the money's currency تغيير وضع العملة النقدية تغيير عملة النقود Layering involves sending the money التصنيف يتضمن ارسال الاموال التصفيف ينطوي على إرسال الأموال make it difficult to follow مما يجعل صعوبة في متابعته تجعل من الصعب متابعة But there were also similarities in the meaning in the two Translations, and it marked a kind of accuracy, and an example of this: Similarities English العربية Global financial markets الاسواق المالية العالمية Money laundering غسيل الاموال Bank-secrecy law قوانين السرية المصرفية Single scheme مخطط واحد Authorities سلطات Criminals مجرمون Embezzlers مختلسون High-value transactions معاملات ذات قيمة عالية High-value items سلع عالية القيمة The main stream economy التيار الرئيسي للاقتصاد Bank-reporting laws قوانين الابلاغ المصرفية foil money-laundering operations. احباط عمليات غسيل الاموال Drug traffickers مهربو المخدرات Financial transactions معاملات مالية Illegal money اموال غير شرعية 1.2.2- Morphology: Concerning the morphology, machine translation was ambiguous in handling word forms, achieving Number and Gender agreement, Identifying the case ending of words, and in the proper handling of pronouns, while human translation has proved its efficiency in giving the exact verb tense number, possession, and the proper gender agreement and comparison. For example, the following sentences show Morphological issues with agglutinative, fusion and complex word structure: Source Sentence: Banks usually lend money to persons who need it, for a specified interest. Google Translate: البنوك عادة إقراض المال إلى الأشخاص الذين يحتاجون إليها، لمصلحة معينة Human Translation : تقوم البنوك باعطاء القروض عادة للأشخاص الذين يحتاجون إليها، في مصلحة معينة At this stage we have to compare the outputs of Google Translate system with the Human Translation. In the first comparison we found that there is: البنوك“ Banks" الذيwho” and “يحتاجون need” are common with human translation but “إقراض المال” and “اعطاء القروض” differs. Source Sentence: The temporary closure of international banks because of the earthquake I Japan, also dented profits. Human translation: وتسبّب الزلزال المدمر الذي ضرب اليابان بالإقفال المؤقت للبنوك الدولية ، الأمر الذي أدّى إلى انخفاض هذه الأرباح Machine translation: إغلاق مؤقت ل بنوك دولي بسبب الزلزال أنا اليابان ، تراجع أيضا الأرباح. Source Sentence: Investors remain cautiously optimistic. Machine translation: ولكن لا يزال المستثمرون متفائلون بحذر . Human translation: إلا أن المستثمرين بقيوا متفائلبن بحذر 1.2.3- Syntax: Concerning syntax, a big difference in the combination of words appears, especially in dealing with Superlative Adjectives, and arranging Nouns and Adjectives and generating Nominal Chunks, hence, the human Translation syntax study is more accurate and follows the grammatical rules: Subject –Verb and we can also notice the proper use of words such determiner, modifier, complement. In the 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, its : Subject-Verb + prepositional phrase. Phrase: NG (noun group), AdjG (adjective group), AdvG (adverb group), PG (prepositional group), PossG (possessive group). -In machine translation, the phrase started with a verb then subject: قد تتكون طبقات من عدة بنوك إلى بنك التحويلات - The usage of prepositions that differs in meaning, but not in structure: ومخطط واحد عادة ما ينطوي على تحويل الأموال من خلال العديد من البلدان- - والسبب في أنه من ضروري - الذي يغسل الأموال وكيف تفعل ذلك 3- the substitution of the preposition, but the reservation of the same structure and the same meaning :مما يجعل من الممكن إيداع مجهول الأموال "القذرة So the syntactic transfer rules that map parse tree for one language into one for another are: – English to Arabic: • NP → Adj Nom Þ NP → Nom ADJ • VP → V NP Þ VP → NP V • PP → P NP Þ PP → NP P 1.2.4- Pragmatics and Comprehension: Concerning Comprehension, the translation must transfer the meaning intended, which makes the comprehension part a major one. Yet, in machine translation, it is not always the case, it produces sometimes sentences, that unfortunately mean something different from the original source text. The majority of machine translation expressions have resulted into a literal and meaningless translation of the saying. For instance, the English say “A good workman is known by his chips”; which has the Arabic meaning as was literally mistranslated into “ومن المعروف ان العامل الجيد من قبل رقائقه”, by the Google translation; which is very literal and very far from the actual meaning of the saying. Source Sentence: The rise of global financial markets makes money laundering easier than ever. Machine Translation : صعود الأسواق المالية العالمية يجعل غسل الأموال أسهل من أي وقت مضى Human Translation : ارتفاع الاسواق العالمية يجعل غسل الاموال اسهل بكثير Source Sentence: In this article, we'll learn exactly what money laundering is and why it's necessary. Machine Translation: في هذه المقالة، وسوف تتعلم بالضبط ما هو غسيل الأموال والسبب في أنه من الضروري، الذي يغسل الأموال وكيف تفعل ذلك Human Translation: في هذه المقالة, سوف تتعلم بالتحديد ما هو غسيل الاموال و ما السبب في انه ضروري Source Sentence: At this stage, the launderer inserts the dirty money into a legitimate financial institution. Machine Translation : في هذه المرحلة، وغاسلي الأموال القذرة يدرج في مؤسسة مالية مشروعة. Human Translation : في هذه المرحلة, يضع غاسلوا الاموال اموالهم في مؤسسة مالية مشروعة Source Sentence: At the integration stage, the money re-enters the mainstream economy in legitimate-looking form. Machine Translation: في مرحلة التكامل، وإعادة الأموال يدخل-التيار الرئيسي للاقتصاد في شكل المشروعة المظهر Human Translation: في مرحلة التكامل, يتم اعادة ادخال الاموال للتيار الرئيسي للاقتصاد في شكل شرعي. According to these examples, we can discern the following differences in meanings: Differences English العربية Human Machine easier than ever اسهل بكثير أسهل من أي وقت the launderer inserts the dirty money , يضع غاسلوا الاموال اموالهم غاسلي الأموال القذرة يدرج the money re-enters , يتم اعادة ادخال الاموال وإعادة الأموال يدخل Hit تلقت تعرضت in legitimate-looking form في شكل شرعي. في شكل المشروعة المظهر We have found out that the overall translation precision for Google was 31,4% , if we considered that the total of words in the two articles is approximately 600 words (504-636 words), and that 188.4 words/ expressions/ morphemes were considered as inaccurate, So the degree of accuracy of the Machine Translation from English to Arabic is around 30%. 2- Recommendations: -It is necessary to improve Machine Translation either, by developing more sophisticated methods or by imposing certain restrictions on the input in order to get a good translation that is fluent, grammatically well structured and readable in the target language. - The translation machine systems (Google, Babylon, tarjim..) should be guiding International economics and should guarantee the fidel translation of documents for businesspeople, bankers, financier and other professionals. - It’s recommended improve new developed features for Translation machines, such as automatic multilingual dictionaries, that handle more support of basic research in computational linguistics. - It is necessary to provide Global Translation service for economic translation of the different types of documents (Accounting statements, balance sheets, auditor’s repots and economis statistics), and this through developed software that are specific to this domain - A Global translation service should provide high-quality translation for any information contained in economic articles, getting it across to the target audience, by including specialized terminology and a good numbering. (statistics sheet) 3-Conclusion: To sum up, English-to-Arabic machine translation has been a challenging research issue for many of the researchers in the field of Arabic Language Processing. In this study, we have concluded that the effectiveness of Machine Translation is limited, furthermore, human translation remains the most accurate and effective in translating technical texts that are related to different domain, as the economic domain . And the human Translation will always be distinct, to an important degree, from the ways in which the major Online Translating Machines as Google Translate, babyllon, etc.. translate a source text in a certain specific domain.. The human translation hence, stays in the first raw in being indispensable for translating data.

Inaam Kuteish and Maha Atwe

Abstract: In line with Inaam Kuteish and Maha Atwe (2013) This paper presents the differences between machine translation and human translation in the literary field. Literary translations are uniquely challenging because they require high-level understanding of specific terms in both the source and target language. In this paper we will tackle semantics, syntax, morphology, comprehensive and terminological differences. Also, we will shed the light on the ways of translating such documents and will show examples differentiating between translated documents. Recommendations are also given in this paper. Sample The Raven by Edgar Allan Poe Once upon a midnight dreary, while I pondered, weak and weary, Over many a quaint and curious volume of forgotten lore, While I nodded, nearly napping, suddenly there came a tapping, As of someone gently rapping, rapping at my chamber door. "'Tis some visitor," I muttered, "tapping at my chamber door - Only this, and nothing more." Ah, distinctly I remember it was in the bleak December, And each separate dying ember wrought its ghost upon the floor. Eagerly I wished the morrow; - vainly I had sought to borrow From my books surcease of sorrow - sorrow for the lost Lenore - For the rare and radiant maiden whom the angels name Lenore - Nameless here for evermore. And the silken sad uncertain rustling of each purple curtain Thrilled me - filled me with fantastic terrors never felt before; So that now, to still the beating of my heart, I stood repeating, "'Tis some visitor entreating entrance at my chamber door - Some late visitor entreating entrance at my chamber door; - This it is, and nothing more." Human translation الغراب شعر: أدغار ألن بو ترجمة: د. إنعام الهاشمي -------------------------------- - 1 - في ليلةٍ مقفرةٍ عند منتصف الليل ، حيث كنت أتأمل وأفكِّر، حائراً، متعباً وضجِر مررتُ بعديدٍ من المعتقداتِ القديمةِ الغريبة – التي غطاها ألنسيان وعندما سقط رأسي على صدري وأوشكتُ أن أغفو فجأةً جاءَ الطرقُ وكأن أحداً برفقٍ يقرعُ .. يقرعُ على بابِ مخدعي تمتمتُ "هو زائرٌ" "هو زائرٌ يقرعُ على بابِ مخدعي – هذا فقط... لا غير" ............................ - 2 - آه.. أتذكّرُ بوضوحٍ إنه ديسمبر الكثيب وفيه كلُّّ جذوةٍ تحتضرُ وتلقي بشبحِها يتمطّى فوقَ الأرض . بلهفةًٍ تمنيتُ الغد؛ – وبعبثٍ حاولتُ أن االتمسَ في كتُبي نهايةًً لهذا الأسى – ألأسى لفقدِ لينور – لتلك الفتاةِ الرائعةِ المتألقة التي أسمتها الملائكةُُ لينور – وهنا لا اسمَ لها ، ابداً بعد اليوم. ................... - 3 - الحفيفُ الحريريُّ الحزينُ والغامضُ لكلِّ ستارةٍ أرجوانية أثارني – ملأني برعبٍ غريب لم أعرفهُ أبدا من قبل ؛ والآن ، لأُهدِّئ دقاتِ قلبي ، وقفتُ أردِّد "هو أحدُ الزوّارِ يرجو الدخولَ في بابِ مخدعي "؛ – هو أحدُ زوّارِالليلِِ يرجو الدخولَ في بابِ مخدعي – هذا هو ، لا غير. Machine translation مرة واحدة عند منتصف الليل الكئيب، وأنا فكرت وضعيفة وأنهكته، أكثر من وحدة تخزين العديد غريبة وغريبة من العلم النسيان، بينما كنت ضربة رأس، القيلولة تقريبا، فجأة هناك جاء التنصت، اعتبارا من بعض واحد موسيقى الراب بلطف، موسيقى الراب في باب حجرتي. "'تيس بعض الزوار،" I تمتم "التنصت على باب حجرتي - هذا فقط، ولا شيء أكثر من ذلك. " آه، بوضوح تذكرت، أنه كان في ديسمبر القابض، ويحدثه كل جذوة مفصولة ميتة شكلت شبحا لها على الأرض. بلهفة تمنيت الغد، - عبثا التمست لاقتراض من surcease كتبي من الحزن - الحزن لفقدان ينور - لالبتول النادرة المتألقة النادرة الذين لينور اسم الملائكة - المجهولون هنا إلى الأبد. وحريري سرقة مؤكد حزينة لكل ستار ارجواني بسعادة غامرة لي - ملأني الرعب رائعة لم أحسها من قبل؛ بحيث الآن، لا يزال لضرب من قلبي، وقفت تكرار، "'تيس بعض الزوار تناشد مدخل عند باب حجرتي - بعض الزوار في وقت متأخر يستجدي مدخل عند باب حجرتي، - هذا هو عليه، وأكثر من ذلك لا شيء. " The article will tackle first Semantics: The word choice in machine translation is very weak where as the human translation which possesses an excellent word choice e.g.: "rapping at my chamber door" " يقرع على باب مخدعي"H.T: "موسيقى الراب في باب حجرتي "M.T: e.g.: "master" "سيد" H.T: "ماجستير"M.T: e.g.: "To the One in Paradise" "الى التي في الفردوس":H.T "الى واحد في الجنة"M.T: e.g.: Fluttering and dancing in the breeze H.T: تراقصُ النسيمَ وتناغي الوتر التصفيق والرقص في النسيم:M.T e.g.: unpurpeled الارجوانية H.T: M.T: unpurpled Syntax: The word order in machine translation is unorganized whereas the word order in human translation is well-organized. e.g.: "He wailed" H.T: "فرد متأسيا" M.T: "ناح انه" e.g.:This it is, and nothing more." H.T: هذا هو ، لا غير. M.T: هذا هو عليه، وأكثر من ذلك لا شيء. " e.g.: "'Tis some visitor," H.T: هو زائرٌ M.T: "'تيس بعض الزوار Morphology: Machine translation has weak grammar unlike human translation. ا E.g.: ", When all at once I saw a crowd" H.T:"فجأة وجدتُ جماعة" M.T:"في كل مرة عندما رأيت الحشد" E.g.: While I nodded, nearly napping, suddenly there came a tapping H.T: وعندما سقط رأسي على صدري وأوشكتُ أن أغفو فجأةً جاءَ الطرقُ M.T: بينما كنت ضربة رأس، القيلولة تقريبا، فجأة هناك جاء التنصت Comprehension: The comprehension level of machine translation is very low unlike the human translation. e.g.: "hon'ring" dids't"" "sent'st" "o'er" "upstarting" Quoth" H.T.: "تكريما اليك" " لم تفعلي" "ارجعته" " فوق" "منتفضاً" "قال " M.T: no translation was given e.g: surcease H.T.: نهايةً M.T: surcease Compatibility: The human translation is 90% compatible with the original text whereas the machine translation is 50% compatible. Analysis From here, we can find that the human translation is different than the machine translation; for the human translator has a mind and can comprehend and know how to make his article coherent and free of errors. The machine translated documents in the literary field are incoherent and lack the sense of comprehension and grammatical coherence. There are many differences between human translation and machine translation, in which errors regarding morphology, syntax, semantics, mechanics, coherence and comprehension are being found. The word choice in machine translation is very weak and the meaning is sometimes vague unlike the human translation which possesses an excellent word choice and uses exact terms that makes the meaning clear. The word order in machine translation is unorganized which makes the sentence barely readable whereas in human translation the word order is well-organized which makes the sentence very clear. Moreover, machine translation has weak grammar such as in sentence structure. . So in machine translation a lot of grammar mistakes are made while a human translator avoids the grammatical mistakes. The comprehension level of machine translation is very low because the machine translation doesn't comprehend the figurative language and the culture especially when it comes to proverbs and sayings unlike human translation. In addition, machine translation is unable to comprehend the old English language like the human translator. In human translation the original text is 90% compatible with the target text whereas in machine translation it is 50% compatible, the text might be explicable but barely readable. Recommendation It is imperative for translators to refrain from using machine translation. In case translators were obliged to seek help they can refer to specialized dictionaries, in addition we recommended to use tradius Moreover, if machine translation is used one should revise the whole text and check the mistakes. It is recommended for those who want to translate texts to seek translators and not machines because a translator can render the original text in an effective and clear way unlike the machine. Although some decoys can be found in the machine translated documents because of the structural changes, these are no more than 5% of the whole document, hence here comes the role of the human translator to detect the errors and correct them. Conclusion To sum up, machine translation has many disadvantages. Machine translation is not reliable; it doesn't give exact meanings, its word order is unorganized, it is weak in structure, it makes many grammar mistakes, and it lacks the ability to comprehend the whole text unlike a human translator who lacks all of these problems. To avoid these problems one should simply resort to human translation. But whenever the translator do his job in a way using his translating skills with the help of the machine, his job will be achieved perfectly without any errors or changes in the meaning. References Hashmy,I. (2011) . The raven. Retrieved on November 24, 2012 from http://www.almothaqaf.com/index.php?option=com_content&view=article&id=6529:2009-10-17-13-32-20&catid=35:2009-05-21-01-46-04&Itemid=0 Hashmy, I.(2011). The raven. Retrieved on July 20,2012 from http://www.babylon-center.net/?articles=topic&topic=556

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.

Reem Bazzal, and Maha Atwi

Wednesday, December 4, 2013

Journalistic Mobile Translation: Success or Failure?"

I. Abstract Mohammad, M., Farhat, A., Hamade, K., Hashem, R. (2013) in the paper entitled " Journalistic Mobile Translation: Success or Failure?" describes the technology available to translators in the first decade of the present century and examines the negative and positive aspects of mobile translation vs. human translation in the domain of journalism. The importance of this technology in discussed based on the idea of devices such as smartphones and tablets providing means for agencies to deliver language capabilities to users anywhere in the world. These ubiquitous devices can easily serve as platforms for translation programs, as a growing number of agencies are equipping personal with mobile devices and translation software that allow them to more efficiently complete their mission, especially as the arena of media deals with languages around the world. I. Commentary Introduction It is an undeniable fact that the globalization brings us to the modern effects such as the invention of mobile phones (MP). Just a couple years ago, we only had simple mobile phone’s application, such as VGA camera, a very limited scope of radio, and improved, in such a way that as a journalist, the target language can be obtained by simply vocally speaking. A- Endorsement Mobile translation is a machine translation service for hand-held devices, including mobile telephones, Pocket PCs, and PDAs. It relies on computer programming in the sphere of computational linguistics and the device's communication means (internet connection or SMS) to work. Such device users are equipped with the advantage instantaneous and non-mediated translation from one human language to another, usually against a service fee that is, nevertheless, significantly smaller that a human translator charges. In the chosen articles above, a significant and very basic role pg Google translation is noticed, that is, in this case the concise translation of general and commonly used proper nouns. For instance, in the article written by Thierry Meyssan, the term "Republican Congress" was translated into "الكونغرس" and it was taken as a proper noun where the first letter of each word is capitalized. Another example on that is the term "United Nations" which was translated directly into "الامم المثحدة". This shows the importance of the mobile translation in terms of accuracy in translating proper and widely known official terms. B- Refutation While many commercial mobile translation options are effective, they often cannot provide the capabilities or security levels that media agencies require. Most text-based mobile machine translation solutions call out to unsecured servers in the cloud over unencrypted phone lines, a serious issue for translating sensitive data. However, the most important challenge facing the mobile translation industry is the linguistic and communicative quality of the translations, although some providers claim to have achieved an accuracy in "understanding" idioms and slang language, machine translation is still distinctly of lower quality than human translation and should be used with care if the matters translated require correctness. III. Recommendation After viewing and researching the above points, on the different and common points between these two very competitive types of translation, the following can be recommended: • Do not utilize without a post-editing stage • Always remember the customer not the budget • A translator should also be objective in his translation so that his personal point of view will not influence the purpose of the main text. • Translators need to accept the new technologies and learn how to use them to their maximum potential as a means to increased productivity and quality improvement. • Utilizing TM software provides users with quick and easy access to a wide array of formats without the need for ownership of the software. • When users employ the likes of TM software for rather large projects, the undertaking is made easier by way of increased savings(time, expense, energy). Thus, there may or may not be a need to translate subsequent versions of large projects. • Users must update their mobile translation software as soon as a new update appears. • Provide more classes on the relations between machine and human translation and more training sessions on the use of the different translation software. IV. Conclusion After comparing the human translated documents with that of the machine, we can deduce that any attempt to replace Human Translation would certainly face failure due to a simple reason; there is no machine translation that is capable of interpreting, beautifying and making the text easy to understand while translating. For instance, it is only the human translator who is able of interpreting certain cultural components that may exist in the source text and that cannot be translated in terms of equivalent terms, just like what automatic translation does, into the language of the target text. In addition, it is widely agreed upon that one of the most difficult tasks in the act of translation is how to keep the same effect left by the source text in the target text. Machine translation, in this regard, has proved its weakness when compared with a human translation. The human translator is the only subject in a position to understand the different cultural, linguistic and semantic factors contributing to leaving the same effect that is left in the source text, in the target text. V. References Shwartz, K. (2013). Fed Tech Magazine. Retrieved from http://www.fedtechmagazine.com. Hermen, J. (2011). Mobile Translation and Its Advantages. Retrieved from http://justinhermen.blogspot.com

Legal translation is the translation of texts within the field of law

I- Abstract Hamade, K., Mohamad, M., Hashem, R., and Farhat, A. (2013), in their " MT a Gift from Heaven to Legal Translators", contains the research and analysis of the common and different points with respect to legal documents. It diagnosis the faults of these documents by human and machine. Meanwhile, the flaws and translation problems are categorized and analyzed, and recommendations are given. Thus, this paper indicates clear linguistic problems in addition to common recommendations as a result to such factors with a slant towards MT's many benefits in this specific category. II- Commentary Legal translation is the translation of texts within the field of law. As law is a culture- dependent subject field, the work of legal translation and its products are not necessarily linguistically transparent. Therefore, translation of such documents has positives and negative aspects that are necessary for a proper, well organized, and meaningful translation. A-Endorsement As legal documents are a case in which time and efficiency greatly matter, the evolved machine translation has played a major role. Cost-effectiveness aside, one of the strongest elements to machine translation is speed. Machine translation allows you to upload multilingual content almost instantly. The addition of translation memories and glossaries into a machine translation package are of great benefit in this category. The premise is still a translation engine like Google but it is now layered with bespoke translations which are specific to holding on to specific forms of commonly used legal documents. These systems can be built into pre-existing translation tools which can then merge with a post-editing system. Essentially, it’s making the hilarious miss-translations a thing of the past. There is much more impressive tech stuff on MT. As seen in the legal document "Apartment Lease Contract" saving such templates is essential for time and word choice as seen in the document more than 50% of the machine translation is similar to that of the human in just a third of the time. For example: "residential unit" was translated by human and by machine as "وحدة سكنية " , which is an overused word by those in the legal field. Google utilizes a majority wins situation where the most common translation across the web is used as translation. Equally, it’s almost tried and tested content with the ‘one-person opinion’ translation over-ruled by the masses which will ultimately be your customer base. Thus this doesn't mean that machine translation should always win over the traditional use of humans. Grammar wise, there are no major mistakes in the translation of Google regarding plural, singular, or verb tenses. However, techniques by humans conquer that of the machine when it comes to proper nouns etc.. which is a major editorial in legal translation. For example, "mister Tarek" was translated correctly as السيد طارق . B- Refutations Machine translation never flows as well as human translation. Despite its evolution, it will still translate the words rather than the concept you are trying to put across. For instance, the in first legal document, when human translated, the translator used various transitional words to ensure the flow of ideas throughout the writing, whereas, in the machine translated, not even a single conjunction or transition was used. For example, the second paragraph in the machine translated legal document started with:"Signed the United Nations", the third paragraph began with:"The General Assembly", as for the fourth paragraph:"The amendment". This shows that machine translation doesn’t give the proper synchronism needed in the article. If a customer searches on Google and finds your company, the first engagement with your company would be tainted by off-message copy and literal translation rather than your proper legal commitment. And it’s also important to remember that customers don’t just enter via your homepage so this is something you should be thinking about at any point of your site. Keywords should always be researched rather than translated to find what local customers are actually searching for. Don’t make the mistake of thinking that the only difference between the legal systems is language! These keywords need to be woven into your content both in meta-data and in-content itself. III- Recommendations In according to the following points much can be advised, especially when it comes to human translators, including: • Integrating MT work into your own for a quicker translation • Don't forget to not completely depend on the machine • Always remember the customer not the budget • Additional training courses for beginners in Legal translation software • Save commonly used templates into your system for a more efficient result IV- Conclusion There are hundreds of pros and cons for both machine translation and human translation approach. Ultimately it comes down to defining factors such as content use, target audience, sector etc. A good mixture of both generally creates an effective multilingual website which doesn’t cost the earth. But content is not one of the areas you always want to scrimp on.