Human VS Machine Translation

Presented by : -Amani carolina Yehya -Douaa Al Ayash -Ramona Shaaban - Amina Al Ashkar - Nivine El Banna I – Abstract This paper compares the human translation with the machine translation. It studies the different aspects of sentences’ structure that are: semantics, syntax, morphology and comprehension. It realizes the big difference in both the meaning and the purpose behind each text. After applying this analysis, results can be clear that no machine translation can result in a credible, meaningful and loyal translation to the source text. This is highly applicable in the legal translation. An immense difference in the quality of translation is obviously realized which led into an incomprehensible translation because of the weakness of the grammatical structure, the word choice, the word order and the lack of coherence.     II. Commentary Translation studies have known the emergence of new methods of translation including the so-called Machine Translation. However, its emergence was not at the expense of Human Translation for the latter proved to be the only subject capable of translating not only by means of substituting words for words, like Machine Translation, but also in terms of respecting linguistic, semantic, and more importantly cultural differences between languages. Actually, before any translation, there should be a full understanding of the source text from the part of the human translator. A. Human VS Machine Translation 1. Analysis Why Human Translations are better than Machine Translations? The limitations of the most popular online translation tools are apparent, but there are more points to consider: • only humans can understand and effectively translate the cultural components of source text to target text. While machine translators can quickly produce target text from inputting source text, the machine does not recognize nor translate idioms, slang, or terms that do not appear in the machine’s memory. • Machine translations are often literal, or word-for-word translations, hence the errors and strange language that often appear. • Human translators can manipulate language in such a way that they mimic the style and purpose of the source text. For example, if the source text is an upbeat promotional piece, a human can reproduce that to create effective materials in the target language. Hatim, B. & Mason, I. (1997) It is conventionally believed that familiarity with the source and target languages, as well as the subject matter on the part of the translator is enough for a good translation. However, due to the findings in the field of text analysis, the role of text structure in translation now seems crucial. To compare and contrast between human translation and machine translation (Google) we must deal with six main parts of the text: semantics, morphology, syntax, mechanics, coherence and thematic links. 2. Morphology We often use final inflections to change an English word's grammatical characteristics, such as the number, tense or voice. English uses prefixing and affixing as the most popular methods of word formation. Arabic uses modeling which means creating words according to the models or patterns. Google translated the word لم يكن متاحا into unreachable. What we realize here is that in Arabic we added لم but in English we added a prefix which is "un". As for the French part then it was translated into inaccessible . In google , the past tense Arabic word اسس into established in English. An "ed" was added to clarify that this verb is in the past tense. This addition is related to the inflectional part of the language. Another word in الأجيال which was translated into generations by adding an s at the end to show that this noun is in its plural form and that’s also related to the inflectional part of the English language. 3. Syntax Classical Arabic tends to prefer the word order VSO (verb before subject) rather than SVO (subject before verb). Subject pronouns are normally omitted except for emphasis or when using a participle as a verb (participles are not marked for person). Auxiliary verbs precede main verbs, and prepositions precede their objects. In human translation, the syntax is well done. However, the Google translation contains syntax mistakes. For example, the sentence “Both parties hereto have hereby agreed that the rental value of the dwelling unit subject to this Agreement shall be of the sum of L.E …” was translated by Google as the following: "كلا الطرفين لهذه الرسالة قد وافقت بموجب هذا أن القيمة الايجارية للوحدة السكنية الخاضعة لهذه الاتفاقية تخضع لل من مجموع جنيه ............... (ليرة فقط ........... المصرية ) ليتم دفعها شهريا ويكون زيادة من قبل ”. However, the translation done by human for this sentence is “اتفق الطرفان على أن تكون القيمة الايجارية الوحدة السكنية موضوع هذا العقد هي مبلغ جنيه (فقط جنيه) شهرياً تزاد بواقع % سنوياً في بداية السنة السنة الثانية . 4. Lexical English words have been traditionally classified into eight lexical categories or parts of speech (and are still done so in most dictionaries): • Noun: any abstract or concrete entity • Pronoun: any substitute for a noun or noun phrase • Adjective: any qualifier of a noun • Verb: any action or state of being • Adverb: any qualifier of an adjective, verb, or other adverb • Preposition: any establisher of relation and syntactic context • Conjunction: any syntactic connector • Interjection: any emotional greeting (or "exclamation") This category contains Arabic parts of speech: Grammatical functions of Arabic words. • Category: Arabic adjectives: Arabic words that give attributes to nouns, extending their definitions. • Category: Arabic adverbs: Arabic words that modify clauses, sentences and phrases directly. • Category: Arabic articles: Arabic words that indicate and specify nouns. • Category: Arabic conjunctions: Arabic words that connect words, phrases or clauses together. Grammar: The example of “human translation” mentioned before is also a fault in grammar. But in the other hand, there are no major mistakes in the translation of Google regarding plural, singular, or verb tenses. However, verb tenses have to be chosen in way that preserves the meaning of the sentence. So from grammatical point of view the sentences translated by Google may be correct, but the meaning may differ if the verb tense is not well chosen. This case may be found in literary text which contains the variety of verb tenses, and, usually, it is not the case in journalistic texts. Word choice: In word choice, Google didn’t choose the accurate word like in the human translation. Names and family names: Sometimes Google translation fails to give the name or the family name of an author, politician, etc … Coherence: Sometimes there is no coherence at all in a text in Arabic already translated from English or French. It is a good example to know how much Google translation may be like a “collection of word” not a sentence well cohered. In the given above machine translated text we see the inappropriate transition from one sentence to another. The relative shortness of the text makes it easier for the machine to translate it and keep the overall meaning coherent, but even in such short texts it is essential to keep the structure and conjunctions that deliver the right meaning. In Arabic language we do not start a new sentence with an adjective or an adverb or a noun, the right structure is: subject- verb- object- complement. Any wrong use of words and transitions makes the text incoherent and hard to follow the meaning. III. Recommendations Translators should recognize and learn to exploit the potential of the new technologies to help them to be more rigorous, consistent and productive without feeling threatened. Some people ask if the new technologies have created a new profession. It could be claimed that the resources available to the translator through information technology imply a change in the relationship between the translator and the text, that is to say, a new way of translating, but this does not mean that the result is a new profession. However, there is clearly the development of new capabilities, which leads us to point out a number of essential aspects of the current situation. Translating with the help of the computer is definitely not the same as working exclusively on paper and with paper products such as conventional dictionaries, because computer tools provide us with a relationship to the text which is much more flexible than a purely lineal reading. Furthermore, the Internet with its universal access to information and instant communication between users has created a physical and geographical freedom for translators that were inconceivable in the past. We share the conviction that translation has not become a new profession, but the changes are here to stay and will continue to evolve. 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. IV. Conclusion Any attempt to replace Human Translation totally by machine translation would certainly face failure for, due to a simple reason, there is no machine translation that is capable of interpretation. 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 can not be translated in terms of equivalent terms, just like what automatic translation does, into the language of the target text.

Recent Trends in Machine Translation”

Written by: Abir HASSSAN- Bassima KHALED- Rania FOUANI   I. Abstract Miller (2008) in his article titled “Recent Trends in Machine Translation” indicated that In the last two years machine translation (MT) has embarked on a voyage into the future, spurred by the presence of personal computers on individual desktops throughout the world and, more recently, universal access to electronic text on-line. This impressive growth has led to many new trends, including major changes in the profile of the user. Apace with this trajectory has come better communication and increased collaboration between all the groups concerned—MT researchers, developers, users, and watchers. The International Association for Machine Translation (IAMT), together with its three regional associations created in 1991, has fostered this convergence by creating opportunities-workshops, conferences, publications—through which to share the latest information in this dynamically growing field. II. Commentary A. New Dimensions in MT Service Delivery a. The dreams of yesteryear's visionaries are finally coming true. Machine translation (MT) has launched on an unparalleled surge of growth—a historic shift in the way it is being used and a phenomenal increase in the number of people who rely on it. We now have MT software that is viable, affordable, and runs on virtually any 1990s desktop. Today there are more than 500 vendors of MT software for the personal computer around the world, and among them they put out well over 1,000 products.3 One of the vendors, Global link, sells its extensive line of software in at least 6,000 stores in North America alone, and at present Europe is its fastest-growing market. The ubiquity of the desktop computer with access to the Internet has given momentum to an unprecedented growth in MT user ship. We now have MT on-line, accessible through e-mail, client server arrangements, Internet service providers, and a growing number of other sites on the Internet. The on-line phenomenon is changing our whole way of thinking about machine translation. Together, these two developments-the abundance of low-cost MT in shrink-wrapped boxes, coupled with MT on-line—are turning machine translation into an everyday commodity that is within the reach of virtually anyone with a late-model personal computer. The sudden shift in MT use and the dramatic increase in its user ship have also brought a sea change in the profile of the user. Because of its widespread availability, MT has been forced, appropriately or not, to graduate from the days when a system's caretakers had to nurture it constantly in order for it to perform acceptably. It now stands on its own, and, by and large, its new users must fend for themselves, whether by customizing the system and/or learning how to post edit, or accepting the output as it is. While all these changes are taking place in MT use, other exciting trends are also redrawing the entire map of the field of machine translation itself. Many languages, especially the more challenging ones, are being tackled and added to the vendors' repertoires. In the new game of "plug-and-play," MT engines are now being made inter faceable with a variety of other software functions. Speech translation is making steady progress. Off-the-shelf tools are speeding up research and development. Creative partnerships are being forged between and within the commercial and academic communities. Systems of different philosophies are being joined together. Indeed, on all fronts MT research is accelerating its ongoing march toward distant horizons. It's safe to say that never in the history of this field has so much happened within such a short period. b. We have all witnessed the explosive expansion of the World Wide Web, the Internet service providers, and, most recently, the intranets. Not many of us, however, are aware of the extent to which machine translation is being swept along in this tide. Already on-line access is causing MT use to grow at an unprecedented rate. As of September 1996, low-cost machine translation in one form or another was available at some 30 on-line sites in cyberspace. It comes in a variety of forms and modalities. MT vendors are also currently gearing up for intranets. On-line purchase is yet another way to go. As with many other kinds of software, the vendors make it easy to order an MT package on- line. In fact, we predict that within a few years the shrink- wrapped box will have yielded almost entirely to on-line sale/purchase arrangements. This growing use of MT on-line cannot be dismissed as casual curiosity. Unlike software purchased off the shelf, for which no direct measurements are possible, on-line access is documented automatically, and therefore patterns can be discerned. For example, the records for CompuServe's production translation service show a number of repeat large-volume users. The statistics (ibid.) reveal that about 85% of the requests are for raw MT—a much larger percentage than had been anticipated. What could not be determined automatically was whether the raw translation was being used for gisting purposes only or whether it was being post edited for further use. To discover more about its subsequent fate, Flanagan conducted a market survey which revealed that the CDTS is used mostly for business and technical purposes where assimilation- quality MT is sufficient (ibid.). The bottom line is that the customer is willing to pay for this service. In the World Community Forum, although there is no direct evidence of the extent to which the machine translations are being relied on, at least one fact can be reported: the Forum's sysop is inundated with complaints on the rare occasions when the MT system goes down. In these circumstances, only a fully automatic process capable of handling very large volumes of text with near-real-time turnaround can provide the translation capacity required by on-line markets. Flanagan (ibid.) also points out that the on-line culture favors rapid and shallow assimilation of information. For these reasons, MT is an ideal fit. B. The New User/Consumer Profile Now that we have seen the new trends in MT from the point of view of the general public, we should look at the perspective of the user and the end consumer. 1. Acceptance: Purpose of translation (a new typology). Traditionally MT usage has been classified according to its purpose. It is considered to be either translation for dissemination, or translation for information purposes only, also known as "gisting" or assimilation. At this point we would like to add a third category and at least two types of each. Type 1 represents the more direct use of MT in one of its natural niches, while type 2 is a further development that requires greater human intervention at some point in the process. Problems: Heavy post editing, judgment calls are time- consuming, domain drift, hence need for improved quality; few systems perform well in this arena; linguistic development investment difficult to target. What it takes: post editing aids; very large and sensitively coded lexicon(s), easy to update (better a combined dictionary than "topical glossaries"); parser and rule base; filters and translation memory also helpful. 2. Assimilation: a. "Raw" MT for gisting, sometimes automated post editing; broad range of subjects. Problems: Quality tends to be poor. What it takes: Very large and judiciously coded lexicon(s), easy to update (better a combined dictionary than "topical glossaries"); parser and rule base. b. Problems: Lack of public awareness of this option; shortage of suitable post editors, translators often not able to relax standards. What it takes: Good quality; large and richly coded dictionaries.  III. Conclusion and Recommendation To sum up, MT Service Delivery has new dimensions where the machine translation launched on an unparalleled surge of growth. In addition to a new User/ Consumer Profile. It is essential for a translator to know that as high-level executives begin to see the huge value and market enabling power of translating large amounts of relevant content, we can expect to see that translation will be viewed as a much more strategic core competence. As this happens, translation professionals could become facilitators and enablers of many key conversations between global enterprises and their customers. The skills required will include the following (and many are just emerging so this is a great opportunity for innovators and leaders): 1. customization of MT systems for specific business purposes; 2. corpus analysis and assessment skills; 3. evolutionary approaches to making high value content multilingual; 4. rapid quality assessment skills; 5. linguistic steering of automated translation systems; 6. community and crowd collaboration management and administration to do a variety of linguistic work; 7. more structured approaches to post-editing MT to enable rapid error identification and correction; 8. continuously evolving and learning MT systems that produce on-going improvements in translation quality; 9. much better and more robust data interchange standards will likely develop Systran: Since September 11, 2001, the warlike spirit which blows on Washington seems to have swept these scruples. Reverso: Since September 11, 2001, the warlike spirit which blows on Washington seem to have swept (annihilated) these scruples. Human translation: Since 11 September 2001 the warmongering mood in Washington seems to have swept away such scruples.   VI. References Anoun, H. (2006). Towards a Logical Approach to Nominal Sentences Analysis in Standard Arabic. In Proceedings of the Eleventh ESSLLI Student Session. Badawi, A. Elsaid, L. Mike, and G. Carter, G. (2004). Modern Written Arabic: A Compre-hensive Grammar. Routledge. Baerman, M. et al (2006). the Syntax-Morphology Interface. A Study of Syncretism. Cambridge Studies in Linguistics. Cambridge University Press. Bar, H. and Yoad, W. (2005). Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages. Ann Arbor, Michigan: Association for Computational Linguistics. (pp. 39, 46) Bar, H. and Yoad, W. (2005). Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages.

Machine Translation (Google) and Human Translation

Abir Zein Zeinab Kabalan Lea Haj Hassan Nabila Wehbe Abstract Translation in the Arab world, for instance, is known as "an act of understanding before explaining". In this regard, it is necessary that before starting the translation of any text, the translator should have a clear understanding, linguistically, semantically and culturally speaking, of that source text so that he or she would be able to convey the real intended meaning of the target language. This paper is an attempt to draw a distinction between Machine Translation and Human Translation shedding light on the different characteristics of each one. Thus, for the sake of illustrating, it will provide many legal texts that are translated by both Machine Translation (Google) and Human Translation. Analysis: It is quite obvious, from the first reading of each translation, that machine translation is not that perfect rendering of the source text into the target text. The point is that the translated text, still, bears much of the traits characterizing the language of the source text; therefore, much should be said about how the use of language is violated as well as the meaning. 1. Semantics: The source text (sample 1): call today for more information! Human translation: أجر الطرف الأول المؤجر للطرف الثاني المستأجر القابل لذلك الوحدة السكنية الموضحة المعالم بالبند التمهيدي Machine translation الطرف الأول، والمؤجر لم تسمح بهذا على الطرف الثاني واستأجرت الطرف الثاني بموجب هذا الاتفاق من المؤجر الوحدة السكنية التي يتم الإشارة إلى الميزات في In the above example, the machine translation is a literal translation or instead a word-for-word translation; the reader can easily notice that there is no flexibility in the machine translation in that each word in the source text has been substituted orderly by another in the machine translation.Thus, it becomes clear that machine translation, is a translation, the focus of which is the source text rather than the target text. The word order is respected only in the source text. 2. Morphology: . Human translation يقر الطرف الثاني المستأجر أنه قد عاين العين المؤجرة المعاينة التامة النافية للجهالة ،وقبل استئجارها بالحالة التي هي عليها ،ويقر أن الوحدة السكنية صالحة للغرض الذي أستؤجرت من أجله ويتعهد بأن يستخدمها فيما حدد لها Machine translation: يجب أن الطرف الثاني أن تعلن اضطلعت الطرف الثاني التفتيش السليم للمباني المؤجرة وقبلت لاستئجار كما هو ويقر بأن الوحدة السكنية ليناسب الغرض الذي تم تأجيره عليه ويتعهد استخدام وحدة حسب ما تم تعيينه. Although the meaning can be comprehensible; nevertheless, the structure of languages are different and, hence, they should be respected for the sake of producing a well-formed translation in the target language. The inability of the machine translation to produce a well-structured text is due to its focus on the "comprehension" and not "the production of a perfect target text". So far as the human translation is concerned, the above example can reveal, clearly how the human translator is capable of avoiding what have been criticized in the machine translation. The human version is a structure respecting and its focus has been in both the source text, in an act of comprehension, and the target text, in an act of producing a perfect translation. The human translator's flexibility allows them to move from language into another bearing in their minds the difference of structures between languages. 3. Syntax: No one can deny that the main rationale behind any translation is to transfer as much as possible the meaning intended by the source text's writer into the target text. Yet, in machine translation, this is not always the case in that sometimes the achieved meaning is ambiguous, distorted, and it becomes difficult to grasp it just like in the following example: The source text (sample 3): particle distribution curve and particle distribution analysis data are not output, and the output is confined to only the CBC 8 parameter. Machine translation: يجب على المستأجر القيام هيربي لدفع أي ضرائب والرسوم والمخالفات والتعويضات أو مصاريف أو الناتجة عن استخدام الوحدة السكنية الخاضعة لهذا الاتفاق من تاريخ توقيع هذا الاتفاق والمؤجر لا يتحمل أي منهم Human translation: يلتزم المستأجر بسداد أية ضرائب أو رسوم أو قيمة أية مخالفات أو غرامات أو تعويضات أو مصروفات تتصل بالوحدة موضوع هذا العقد أو تنتج عن استخدامه لها من تاريخ التوقيع على هذا العقد ولا يتحمل المؤجر بأي منها In this example, the machine translated sentence produces certain associations with no sense. This is mainly, as stated before, due to the fact that machine translation focuses on the source text's language which is in this case English, as being different from Arabic. As for the human translation in the same example, the ability of the translator to substitute the words renders the translation easy to be understood. it is only through human translation that the translator can add or delete certain words or even phrases, sometimes, for the sake of clarity. Conclusion: Finally, it is obvious that the machine translation can’t compete with the human capacity to produce and interpret words and languages, because the meanings are missed in many phases of translation also the translation is limited and can’t recognize nor translate some words like idioms and slang for example. On one hand, someone said that: “Machine translation is an important but difficult problem. One of the properties that makes it difficult is the fact that different languages express the same concepts in different orders. A machine translation system must therefore rearrange the source language concepts to produce a fluent translation in the target language.” On the other hand, human translators can reproduce different words to create effective materials in the target language. While the Machine translations are useful in giving the reader a general idea of what the source text says, but can never replace the human element in translation, and only a human translator can render a translation suitable for public consumption. Recommendations: It is recommended that translators with the help of specialists and expert to upload and update their memories for machine translation, regularly. It is recommended as well that translators pinpoint the weaknesses and mistakes of machine translation, in order to go back revise and check these points. Programmers have to stop creating programs that seek to replace human translators for the reasons mentioned above. And the programs that are made need to be of such a nature that human translators can make use of them to enhance the translation process and make it easier and more accurate, like it is being done with translation memories.

Trends of MT

Abstract Douaa Al ayash(2013) said that these days the most important cause of economic wealth is information and its access.This is a real state that we are in not only for the world,but for the countries that are willing to achieve good economical state and to improve their business.Here,it is important to stop at this point and take look at the cause behind the improvement of business related to professional translation.Machine translation is one of the technologies that are causing a better professional translation achieving knowledge an economic issues as well. Nowadays , we can find a huge encyclopedia spread all over the world,which is Internet.Information is spread in a large number and that encourages knowledge.Thus,it is very important to make a successful usage of this information to get a multilingual information.

Arabic Computational Linguistics

Abstract: Jana Issa(2013) said that a translation memory consists of text segments in a source language and their translations into one or more target languages. These segments can be blocks, paragraphs, sentences, or phrases. Individual words are handled by terminology bases and are not within the domain of TM. In this document we indicate the using of Translation Memories (TM), its history, and the benefits. We can mention only the most significant research systems and projects and only the most important operating and commercial systems.

New Trends of Machine Translation

I. Abstract Miller (2010) stressed in his article on the current machine translation systems. The author explained the components of a machine translation system from the standpoint of software, linguistic components, and users' demands. The importance of pre-editing and post-editing is stressed. The semantic and contextual processings are essential to obtain a better translation quality, which are the future problems to attack. Attention is given to the difficulty of contemplating a pivot method in machine translation instead of transfer methods, because the projection from a word or a phrase to a concept is very difficult if we want to have a very exact concept representation and translation. A new transfer method which accompanies the pe-transfer structural adjustment and post-transfer adjustment is explained... Systems always are imperfect, and users must use them after recognizing the possibilities and the limitations of the system, that’s why various trends in this domain will be discussed in this notation. Nivine El Banna

Computer-Assisted Translation

Computer-Assisted Translation aims at presenting the specificity of computer-assisted translation and to initiate students to the use of the main MT tools .