CHAPTER FIVE CONCLUSION, FINDINGS, and RECOMMENDATIOINS 5.1 Introduction 49 5.2 Conclusion 49 5.3 Findings: 49 5.3 Recommendations: 50 References: 51 Appendixes 53

Developing EFL learners Awareness about Machine Translation Problems

(A case Study of Gezira University Students , Faculties of Education Hasaheisa, Sudan, 2014)

Mohamed Adam Farajallah Kuku

Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Arts in English Language Teaching (ELT)

Department of Foreign Languages Faculty of Education, Hasaheisa University of Gezira

5102

Developing EFL learners Awareness About Machine Translation Problems

(A case Study of Gezira University Students , Faculties of Education Hasaheisa, Sudan, 2014)

Mohamed Adam Farajallah Kuku

Supervision Committee

Name Position Signature 1- Dr. Mubarak Siddique Main Supervisor ……………

2- Dr. Ahmed Gasmal Seed Co- Supervisor ……………

2015

Developing EFL learners Awareness About Machine Translation Problems

(A case Study of Gezira University Students , Faculties of Education Hasaheisa, Sudan, 2014)

Mohamed Adam Farajallah Kuku

Examination Committee

Name Position Signature 1- Dr. Mubarak Siddig chairperson

2- Dr. external examiner

3- Dr. internal examiner

Date of examination / /2015

3 Table of contents

Subject Page CHAPTER ONE INTRODUCTION 1.0 Background 1 1.1 Statement of the problem 2 1.2 Objectives of the study 2 1.3 Questions of the study 2 1.4 Hypotheses of the study 2 1.5 Significance of the study 3 1.6 Methodology 3 1.7 Limitations of the study 3 CHAPTER TWO LITERATURE REVIEW 2.0 Introduction 4 2.1 Definition 4 2.1.1 The term and the concept of "translation" 4 2.1.2 Common misconceptions 5 2.3: History of Translation 6 2.4 Translation and Interpretation: 7 2.5 Translation process 8 2. 5.1measuring success in translation 9 2.5 Specialized types of translation 11 2.5.1 Administrative translation 11 2.5.2 Commercial translation 11 2.5.3 Computer translation 12 2.5.4 General translation 12 2.4.5 Legal translation 12 2.5.6 Literary translation 12 2.5.7Medical translation 14 2.5.8 Pedagogical translation 14

4 2.5.9 Scientific translation 15 2.5.10 Scholarly translation 15 2.5.11 Technical translation 15 2.5.12 Translation for dubbing and film subtitles 15 2.5.13 Translation of religious texts 16 2.5.14 Machine translation 17 2.5.14.1 Computer-assisted translation 17 2.5.15 Cultural translation 18 2.6 : 2.6 Machine translation 19 translator supports the machine. 19

2.6.1 Computer-assisted translation 19 2.7 Translation problems 21 2.7.1 General problems 21 2.7.2 The problem of "untranslatability" 22 2.5.16 Criticism of Machine Translation 24 Machine Translation Strategies 28 CHAPTER THREE METHODOLOGY 3.0 Introduction 34 3.1 Sampling 34 3.3 Tools for collecting Data 34 3.3.1 Contents of the Questionnaire 34 3.3.1.1 Validity of the Questionnaire 34 3.3.1.2 Reliability of the Questionnaire 35 3.4 Instrument for Data Analysis 36

CHAPTER FOUR DATA PRESENTATION AND ANALYSIS 4. 0 Introduction 37 4.1Data analysis and Dissection 37

CHAPTER FIVE CONCLUSION, FINDINGS, AND RECOMMENDATIOINS 5.1 Introduction 49 5.2 Conclusion 49 5.3 Findings: 49 5.3 Recommendations: 50 References: 51 Appendixes 53

6 Developing EFL Learner's Awareness about Machine Translation Problems ( A Case Study of Faculty of Education, Hantoub, Gezira, Sudan, 2014) Mohamed Adam Faraj Allah Kuku M.A. in ELT, 2014 Department of English Language Hasaheisas University of Gezira

Abstract The importance of Machine Translation in communities where more than one language is generally spoken, as these communities often experience in a high need for routine translation. The study aims at enhancing the classical MT models, to introduce syntactical knowledge in the pre- translation step by reordering the source side of the corpus, determining the potential of different language model (ML) enhancement techniques in order improve the performance and efficiency of MT, and to present a continuous – space LM, estimated in the form of an artificial neutral network. The study followed the Descriptive Analytical Method. A questionnaire is used as a tool of data collection (20 MA students). The sample has been chosen randomly for the study population. The collected data were analyzed by using computer programme (SPSS). Among the final findings there are many results; Machine translation is valid for certain absolutely students encounter difficulties when using MT, problems of using MA results from the programme. Machine translating suitable words training. MT spent more time in amending and substituting suitable words, students are not allowed to use MT in literary works there is a range of inability to translate accurately needs students to resort MT. and MT is publicly available through tools on the internet such as Google Translation, Babel Fish. The study recommends the followings; avoid using MT in translating long texts, revise all the materials if translated by MT, students should not depend completely on MT, Designing a well belt infrastructure to cope with ICT development, Enhance electronic systems to support translation programmes, students should not use machine permanently, teachers should be trained, MT spent more time in amending and substituting suitable vocabulary and students are not allowed to use MT in literary works.

7 8

CHAPTER ONE INTRODUCTION 1.0 BACKGROUND: Machine Translation is a field of computational linguistics that investigates the translation of texts from one human language to another, while Statistical Machine Technology, in contrast to many automatic rule-based translation systems, is a translation paradigm based on statistical learning techniques. Our world is currently in a period of globalization, which implies increasing interaction and the intertwining of different language communities. Information globalization extends to all corners of the world, and although English is becoming a universal second language, users in general still feel more comfortable in their own native language. Consequently, multi-linguality should be seen as a strategic issue for all companies aiming to play an important role in the future information society. Poysti’s1 states that ―you can always buy in your own language, but you must sell in your customer’s language―has become more and more relevant these days. A modern conception of social communications must include engaging customers, including commercial companies and users, in any information in its textual representation regardless of geography and cultural expectations. Another important aspect is the socio-political importance of translation in communities where more than one language is generally spoken, as these communities often experience a high need for routine translation. MT is particularly attractive for the European Union (EU) since it already experiences high demands in terms of translation; as of January 1, 2007, there are 23 official EU working languages, and there are significant

9 improvements have been achieved in machine translation (MT) over the past few years, mostly motivated by the appearance of statistical machine translation (SMT) technology, which is currently considered the best way to perform MT of natural languages. 1.5 Statement of the problem : The researcher noticed that, the majority of university students, tend to use computer in translating their language tasks in translation, without caring for what they take from these sites they do not confirm whether it is true or false, acceptable or not. For example, Google Translation gives as a crude, erroneous translation that doesn't follow the correct grammatical rules. The student's weak knowledge renders them unable to see these defects. This creates a problem. Another issue is how machine translation can be of use to minimize the translator's efforts in large scale enterprises. Because it is necessary to get help from the machine and then the translator interferes to amend the final draft. This creates a problem that may face graduates in their future when they depend mostly in those unconfirmed methods. 1.6 Objectives of the study: 1. for enhancing the production of MT 2. For introducing syntactical knowledge in the pre-translation step by reordering the source side of the corpus. 3. for improving the performance and efficiency of MT 4. For presenting continuous-space, estimated in the form of an artificial neural network. 1.7 Questions of the study : 1- What is the concept in machine translation? 2- What are the main problems that face students when using MT?

11 3- do the students gain benefits from using MT? 4- What are the advantages and disadvantages of using MT? 1.8 Hypotheses of the study 1. The student's awareness of language syntactically, semantically, and stylistically should be developed, or else they will not be able to amend errors of Machine Translation.

2. Machine Translation is badly needed for giving the gist of the text, and so minimizes the translator's effort and time.

3. The machine is indispensable for present day educational activities.

4. Some computer programs can find good translation, for single words and scientific learners.

5. MT is unable to convey idiomatic translation.

1.5 Significance of the study:

The study tries to find out how best the students can make use of machine translation, and these necessities improving their competence in the four language skills.

1.6 Methodology:

The researcher will follow the descriptive analytic method, for it is suitable for this type of research.

1.7 Limitations of the study:

The study is limited to the students of Gezira University – Faculty of

Education – Hasaheisa, 2014

Chapter two

Literature Review

2.0 Introduction:

Machine translation is a controversial issue.

2.1 Definition:

Translation is an activity comprising the interpretation of the meaning of a text in one language — the source text — and the production, in another language, of a new, equivalent text — the target text, or translation. (Michael,2007)

Traditionally, translation has been a human activity, although attempts have been made to automate and computerize the translation of natural language texts — machine translation — or to use computers as an aid to translation — computer-assisted translation. The goal of translation is to establish a relation of equivalence of intent between the source and target texts (that is to say, to ensure that both texts communicate the same message), while taking into account a number of constraints. These constraints include context, the rules of grammar of languages, their writing conventions, their idioms, and the like. (Michael, 2007)

2.1.1 The term and the concept of "translation" "Translation" is, etymologically, a "carrying across" or "bringing across": the Latin translatio derives from transferre (trans, "across" + ferre, "to carry" or "to bring"). The modern European languages, Romance, Germanic and Slavic, have generally formed their own equivalent terms for this concept after the Latin model: after transferre or after the kindred traducere ("to lead across" or "to bring across"). Additionally, the Greek term for "translation," metaphrasis (a "speaking

12 across"), has supplied English with "metaphrase," meaning a literal, or word-for-word, translation, as contrasted with "paraphrase" (a "saying in other words," from the Greek paraphrasis).

2.1.2 Common misconceptions Many newcomers to translation wrongly believe it is an exact science, and mistakenly assume a firmly defined one-to-one correlation exists between the words and phrases in different languages which make translations fixed, much like cryptography. In that vein, many assume all one needs to translate a given passage is to decipher between the languages using a translation dictionary. On the contrary, such a fixed relationship would only exist were a new language synthesized and continually synchronized alongside an existing language in such a way that each word carried exactly the same scope and shades of meaning as the original, with careful attention to preserve the etymological roots, assuming they were even known with certainty. In addition, if the new language were ever to take on a life of its own apart from such a strict cryptographic use, each word would begin to take on new shades of meaning and cast off previous associations, making any such synthetic synchronization impossible. As such, translation from that point on would require the disciplines described in this article. Suffice it to say, while equivalence is sought by the translators, less rigid and more analytical methods are required to arrive at a true translation.

There is also debate as to whether translation is an art or a craft. Literary translators, such as Gregory Rabassa in "If This Be Treason" argue convincingly that translation is an art, though he acknowledges that it is teachable. Other translators, mostly professionals working on technical, business, or legal documents, approach their task as a craft, one that can not only be taught but is subject to linguistic analysis and

13 benefits from academic study. Most translators will agree that the truth lies somewhere between and depends on the text. A simple document, for instance a product brochure, can be quickly translated in many cases using simple techniques familiar to advanced language students. By contrast, a newspaper editorial, text of a speech by a politician, or book on almost any subject will require not only the craft of good language skills and research technique but also the art of good writing, cultural sensitivity, and communication.

2.3: History of Translation:

Eugene Nida (1959-1998:12-23) places the beginning of translation with the production of the Septuagint which seems to have been the first translation of the Hebrew Old Testament into Greek.

It was carried out by seventy-two translators, and it provides us with the basic categories of the history of this practice.

Nida also, states that translation itself was a «science», a theory that was subsequently rejected by others in the second half of the century

Following Douglas Robinson’s definition (1997, 2002), the history of translation goes back to the ancient times with the distinction of «word- for-word» (literal translation or verbum pro verbo) and «sense-for-sense» (free translation or sensum pro sensu) employed for the first time by MarcusTullius Cicero (106-43 B.C.E) in his De optimo genera oratorum (The BestKind of Orator, 46 B.C.E) and translated by H.M. Hubbell. Cicero pointed out that one should not translate word-to- word and opened a debate that has continued for centuries. Long after Cicero made his statement, the same issues were still discussed since, the scholar Peter (1988b) claimed, in the second half of the 20th century, that the main

14 problem of translating a textwas «whether to translate literally of freely» (1988b: 45).

Add also the period of the Abbasid Khaliphate, where there were two schools of translation during Al-Mamoon's era. Hunain Ibn Ishag adopted sense for sense translation. Yohanna Ibn ALbitrooq adopted word for word (Maurice,1969)

It is important to cite Horace, Pliny, Quintilian, St. Augustine, Saint. Jerome, John Dryden Miguel de Cervantes, Novalis, Johann Wolfgang von Goethe, Percy Bys she Shelley, Aryeh Newman, Ezra Pound, etc, for being thinkers who dealt with the subject of translation. The etymology of translation, trans-ducere, means to «bring across». Nida defines the concept in a more systematic way:

Translating consists in reproducing in the receptor language the closest natural equivalent of the source language message, first in terms of meaning and secondly interms of style. But this relatively simple statement requires careful evaluation of several seemingly contradictory elements (1969, 1982: 12).

2.4 Translation and Interpretation: A distinction is made between translation, which consists of transferring from one language to another ideas expressed in writing, and interpreting, which consists of transferring ideas expressed orally or by the use of gestures (as in the case of sign language).

Although interpreting can be considered a subcategory of translation in regard to the analysis of the processes involved (translation studies), in practice the skills required for these two activities are quite different. Translators and interpreters are trained in entirely different manners. Translators receive extensive practice with representative texts in various subject areas, learn to compile and manage glossaries of

15 relevant terminology, and master the use of both current document- related software (for example, word processors, desktop publishing systems, and graphics or presentation software) and computer-assisted translation (CAT) software tools.

Interpreters, by contrast, are trained in precise listening skills under taxing conditions, memory and note-taking techniques for consecutive interpreting (in which the interpreter listens and takes notes while the speaker speaks, and then after several minutes provides the version in the other language), and split-attention for simultaneous interpreting (in which the interpreter, usually in a booth with a headset and microphone, listens and speaks at the same time, usually producing the interpreted version only seconds after the speaker provides the original).

The industry expects interpreters to be about 70% accurate; that is to say that interpretation is an approximate version of the original. Translations should be over 99% accurate, by contrast.

2.5 Translation process The translation process, whether it be for translation or interpreting, can be described simply as:

1. Decoding the meaning of the source text, and 2. Re-encoding this meaning in the target language. To decode the meaning of a text the translator must first identify its component "translation units", that is to say the segments of the text to be treated as a cognitive unit. A translation unit may be a word, a phrase or even one or more sentences. Behind this seemingly simple procedure lies a complex cognitive operation. To decode the complete meaning of the source text, the translator must consciously and methodically interpret

16 and analyze all its features. This process requires thorough knowledge of the grammar, semantics, syntax, idioms and the like of the source language, as well as the culture of its speakers.

The translator needs the same in-depth knowledge to re-encode the meaning in the target language. In fact, often translators' knowledge of the target language is more important, and needs to be deeper, than their knowledge of the source language. For this reason, most translators translate into a language of which they are native speakers.

In addition, knowledge of the subject matter being discussed is essential.

In recent years studies in cognitive linguistics have been able to provide valuable insights into the cognitive process of translation.

2. 2.0measuring success in translation As the goal of translation is to ensure that the source and the target texts communicate the same message while taking into account the various constraints placed on the translator, a successful translation can be judged by two criteria:

1. Faithfulness, also called fidelity, which is the extent to which the translation accurately renders the meaning of the source text, without adding to it or subtracting from it, and without intensifying or weakening any part of the meaning; and 2. Transparency, which is the extent to which the translation appears to a native speaker of the target language to have originally been written in that language, and conforms to the language's grammatical, syntactic and idiomatic conventions. (L’épreuve de l’étranger, 1984),

17 A translation meeting the first criterion is said to be a "faithful translation"; a translation meeting the second criterion is said to be an "idiomatic translation". The two are not necessarily exclusive.

The criteria used to judge the faithfulness of a translation vary according to the subject, the precision of the original contents, the type, function and use of the text, its literary qualities, its social or historical context, and so forth.

The criteria for judging the transparency of a translation would appear more straightforward: an unidiomatic translation "sounds" wrong, and in the extreme case of word-for-word translations generated by many machine translation systems, often result in patent nonsense with only a humorous value.

Nevertheless, in certain contexts a translator may knowingly strive to produce a literal translation. For example, literary translators and translators of religious works often adhere to the source text as much as possible. To do this they deliberately "stretch" the boundaries of the target language to produce an unidiomatic text. Likewise, a literary translator may wish to adopt words or expressions from the source language to provide "local colour" in the translation.

The concepts of fidelity and transparency are looked at differently in recent translation theories. The idea that acceptable translations can be as creative and original as their source text is gaining momentum in some quarters.

In recent decades, the most prominent advocates of non- transparent translation modes include the Franco-Canadian translation scholar (Antoine Berman, 1984) who identified twelve deforming tendencies inherent in most prose translations, and the American theorist

18 Lawrence Venuti who called upon translators to apply "foreign zing" translation strategies instead of domesticating ones.

Many non-transparent translation theories draw on concepts of German Romanticism, with the most obvious influence on latter-day theories of foreignization being the German theologian and philosopher Friedrich Schleiermacher. In his seminal lecture "On the Different Methods of Translation" (1813) he distinguished between translation methods that move "the writer toward [the reader]", i.e. transparency, and those that move the "reader toward [the author] ", i.e. respecting the foreignness of the source text. Schleiermacher clearly favoured the latter. It is worth pointing out, however, that his preference was motivated not so much by a desire to embrace the foreign but was rather intended as a nationalist practice to oppose France's cultural domination and to promote German literature.

The concepts of fidelity and transparency remain strong in Western traditions, however. They are not necessarily as prevalent in non-Western traditions. For example, the Indian epic Ramayana has numerous versions in many Indian languages and the stories in each are different from one another. If one looks into the words used for translation in Indian (either Aryan or Dravidian) languages, the freedom given to the translators is evident.

2.5.2 Specialized types of translation Any type of written text can be a candidate for translation, however, the translation industry is often categorized by a number of areas of specialization. Each specialization has its own challenges and difficulties. An incomplete list of these specialized types of translation includes:

2.5.3 Administrative translation

19 The translation of administrative texts.

2.5. 4Commercial translation The translation of commercial (business) texts. This category may include marketing and promotional materials directed to consumers.

2.5.5 General translation The translation of "general" texts. In practice, few texts are really "general"; most fall into a specialization but are not seen as such.

2.5.6 Legal translation Main article: Legal translation The translation of legal documents (laws, contracts, treaties, etc.).

A skilled legal translator is normally as adept at the law (often with in- depth legal training) as with translation, since inaccuracies in legal translations can have serious results.

(One example of problematic translation is the Treaty of Waitangi, where the English and Maori versions differ in certain important areas.)

Sometimes, to prevent such problems, one language will be declared authoritative, with the translations not being considered legally binding, although in many cases this is not possible, as one party does not want to be seen as subservient to the other.

2.5. 7 Literary translation The translation of literary works (novels, short stories, plays, poems, etc.)

If the translation of non-literary works is regarded as a skill, the translation of fiction and poetry is much more of an art. In multilingual countries such as Canada, translation is often considered a literary pursuit in its own right. Figures such as Sheila Fischman, Robert Dickson and

21 Linda Gaboriau are notable in Canadian literature specifically as translators, and the Governor General's Awards present prizes for the year's best English-to-French and French-to-English literary translations with the same standing as more conventional literary awards.

Writers such as Vladimir Nabokov have also made a name for themselves as literary translators.

Many consider poetry the most difficult genre to translate, given the difficulty in rendering both the form and the content in the target language. In 1959 in his influential paper "On Linguistic Aspects of Translation", the Russian-born linguist and semiotician Roman Jakobson even went as far as to declare that "poetry by definition [was] untranslatable". In 1974 the American poet James Merrill wrote a poem, "Lost in Translation," which in part explores this subject. This question was also explored in Douglas Hofstadter's 1997 book, Le Ton beau de Marot.

Translation of sung texts — sometimes referred to as a "singing translation" — is closely linked to translation of poetry, simply because most vocal music, at least in the Western tradition, is set to verse, especially verse in regular patterns with rhyme. (Since the late 19th century musical setting of prose and free verse has also come about in some art music, although popular music tends to remain conservative in its retention of stanzaic forms with or without refrains.) A rudimentary example of translating poetry for singing is church hymns, such as German chorales translated into English by Catherine Wink worth.

Translation of sung texts is generally much more restrictive than translation of poetry, because in the former there is little or no freedom to choose between a versified translation and a translation that dispenses with verse structure. One might modify or omit rhyme in a singing

21 translation, but the assignment of syllables to specific notes in the original musical setting places great challenges on the translator. There is the option in prose, less so in verse, of adding or deleting a syllable here and there by subdividing or combining notes, respectively, but even with prose the process is nevertheless almost like strict verse translation because of the need to stick as close as possible to the original prosody. Other considerations in writing a singing translation include repetition of words and phrases, the placement of rests and/or punctuation, the quality of vowels sung on high notes, and rhythmic features of the vocal line that may be more natural to the original language than to the target language.

Whereas the singing of translated texts has been common for centuries, it is less necessary when a written translation is provided in some form to the listener, for instance, as inserts in concert programs or as projected titles in performance halls or visual media.

2.5.8Medical translation The translation of works of a medical nature.

Like pharmaceutical translation, medical translation is specialization where a mistranslation can have grave consequences.

2.5.9 Pedagogical translation Translation practised as a means of learning a second language.

Pedagogical translation is used to enrich (and to assess) the student's vocabulary in the second language, to help assimilate new syntactic structures and to verify the student's understanding. Unlike other types of translation, pedagogical translation takes place in the student's native (or dominant) language as well as the second language. That is to say that the student will translate both to and from the second language. Another difference between this mode of translation and other modes is that the

22 goal is often literal translation of phrases taken out of context, and of text fragments, which may be completely fabricated for the purposes of the exercise.

Pedagogical translation should not be confused with scholarly translation.

2.5.10 Scientific translation The translation of scientific texts.

2.5.11 Scholarly translation The translation of specialized texts written in an academic environment.

Scholarly translation should not be confused with pedagogical translation.

2.5.12 Technical translation The translation of technical texts (manuals, instructions, etc.).

More specifically, texts that contain a high amount of terminology, that is, words or expressions that are used (almost) only within a specific field, or that describe that field in a great deal of detail.

2.5.13 Translation for dubbing and film subtitles Dialogs and narrations of feature movies and foreign TV programs need to be translated for the local viewers. In this case, translation for dubbing and translation for film subtitles demand different versions for the best effect.

2.5.14 Cultural translation This is a new area of interest in the field of translation studies. Cultural translation is a concept used in cultural studies to denote the process of transformation, linguistic or otherwise, in a given culture. The concept uses linguistic translation as a tool or metaphor in analysing the

23 nature of transformation in cultures. For example, ethnography is considered a translated narrative of an abstract living culture.

2.5.15 Translation of religious texts The translation of religious works has played an important role in world history. For instance the Buddhist monks who translated the Indian sutras into the Chinese language would often skew the translation to better adapt to China's very different culture. Thus notions such as filial piety were stressed.

One of the first instances of recorded translation activity in the West was the rendition of the Old Testament into Greek in the third century B.C.E.; this translation is known as the Septuagint, alluding to the seventy translators (seventy-two in some versions) that were commissioned to translate the Bible on the island of Paphos, with each translator working in solitary confinement in a separate cell. Legend has it that all seventy versions were exactly identical. The Septuagint became the source text for later translations into many other languages including Latin, Coptic, Armenian, and Georgian.

St. Jerome, the patron saint of translation, is still considered one of the greatest translators in history for his work on translating the Bible into Latin. The Catholic Church used this translation (known as the Vulgate) for centuries, but even his translation met much controversy when it was released.

The period prior to and contemporary with the Protestant Reformation saw the translation of the Bible into the local languages of Europe, an act that had a great impact on the split between Protestantism and Catholicism, owing to the divergences between the Protestant and Catholic translations of particular words and passages of the Bible.

24 Martin Luther's Bible in German, Jakub Wujek's Bible in Polish, and the King James Bible in English had lasting effects on the religion, culture, and language of those countries.

2.6 Machine translation Machine translation (MT) is a form of translation where a computer program analyses the source text and produces a target text without human intervention.

2.6.1 Computer-assisted translation Computer-assisted translation (CAT), also called computer-aided translation, is a form of translation where a human translator creates a target text with the assistance of a computer program. Note that in computer-assisted translation, the machine supports an actual, human translator.

Computer-assisted translation can include standard dictionary and grammar software; however, the term is normally used to refer to a range of specialized programs available for the translator, including translation memory, terminology management and alignment programs.

Translation memory (TM) programs store previously translated source texts and their equivalent target texts in a database and retrieve related segments during the translation of new texts. Such programs split the source text into manageable units known as "segments." A source-text sentence or sentence-like unit (headings, titles or elements in a list) may be considered a segment, or texts may be segmented into larger units such as paragraphs or small ones, such as clauses. As the translator works through a document, the software displays each source segment in turn and provides a previous translation for re-use, if the program finds a matching source segment in its database. If it does not, the program allows the translator to enter a translation for the new segment. After the

25 translation for a segment is completed, the program stores the new translation and moves onto the next segment. The translation memory, in principle, is a simple database fields containing the source language segment, the translation of the segment, and other information such as segment creation date, last access, translator name, and so on.

Some translation memory programs function as stand alone environments, while others function as an add-on or macro to commercially available word-processing or other business software programs. Add-on programs allow source documents from other formats, such as desktop publishing files, spreadsheets, to be handled using the TM program.

Terminology management software provides the translator a means of automatically searching a given terminology database for terms appearing in a document, either by automatically displaying terms in the translation memory software interface window or through the use of hot keys to view the entry in the terminology database. Some programs have other hotkey combinations allowing the translator to add new terminology pairs to the terminology database on the fly during translation.

Alignment programs take completed translations, divide both source and target texts into segments, and attempt to determine which segments belong together in order to build a translation memory database with the content. The resulting TM can then be used for future translations.

2.6.2. Procession Machine translation

In recent years machine translation, a major goal of natural language processing, has met with limited success. Most machine translation involves some sort of human intervention, as it requires a pre-

26 editing and a post-editing phase. Note that in machine translation, the translator supports the machine.

Tools available on the Internet, such as AltaVista's Babel Fish, and low- cost translation programs, have brought machine translation technologies to a large public. These tools produce what is called a "gisting translation" — a rough translation that gives the "gist" of the source text, but is not otherwise usable.

However, in fields with highly limited ranges of vocabulary and simple sentence structure, for example weather reports, machine translation can deliver useful results.

Engineer and futurist (Kurzweil, 2012) has predicted that:

''by 2012 machine translation will be powerful enough to dominate the translation field. MIT's Technology Review also listed universal translation and interpretation as likely "within a decade" in its 2004 list. Such claims however have been made since the first serious forays into machine translation in the 1950s.''

2.6.3 Range of Computer-assisted translation Computer-assisted translation can include standard dictionary and grammar software; however, the term is normally used to refer to a range of specialised programs available for the translator, including translation memory, terminology management and alignment programs.

27 and provides a previous translation for re-use, if the program finds a matching source segment in its database. If it does not, the program allows the translator to enter a translation for the new segment. After the translation for a segment is completed, the program stores the new translation and moves onto the next segment. The translation memory, in principle, is a simple database fields containing the source language segment, the translation of the segment, and other information such as segment creation date, last access, translator name, and so on.

Terminology management software provides the translator with means of automatically searching a given terminology database for terms appearing in a document, either by automatically displaying terms in the translation memory software interface window or through the use of hot keys to view the entry in the terminology database. Some programs have other hotkey combinations allowing the translator to add new terminology pairs to the terminology database on the fly during translation.

2.6.4 Machine Translation Strategies

28 Machine translation is an autonomous operating system with strategies and approaches that can be classified as follows:

 the direct strategy

 the transfer strategy

 the pivot language strategy

The direct strategy, the first to be used in machine translation systems, involves a minimum of linguistic theory. This approach is based on a predefined source language-target language binomial in which each word of the source language syntagm is directly linked to a corresponding unit in the target language with a unidirectional correlation, for example from English to Spanish but not the other way round. The best-known representative of this approach is the system created by the University of Georgetown, tested for the first time in 1964 on translations from Russian to English. The Georgetown system, like all existing systems, is based on a direct approach with a strong lexical component. The mechanisms for morphological analysis are highly developed and the dictionaries extremely complex, but the processes of syntactical analysis and disambiguation are limited, so that texts need a second stage of translation by human translators. The following is an example that follows the direct translation model:

Source language text La jeune fille a acheté deux livres Breakdown in source language La jeune fille acheter deux livre Lexical Transfer The young girl buy two book

29 Adaptation in target language The young girl bought two books

There are a number of systems that function on the same principle: for example SPANAM, used for Spanish-English translation since 1980, and SYSTRAN, developed in the United States for military purposes to translate Russian into English. After modification designed to improve its functioning, SYSTRAN was adopted by the European Community in 1976. At present it can be used to translate the following European languages:

 Source languages: English, French, German, Spanish, Italian, Portuguese, and Greek.

 Target languages: English, French, German, Spanish, Italian, Portuguese, Greek, Dutch, Finnish, and Swedish.

In addition, programs are being created for other European languages, such as Hungarian, Polish and Serbo-Croatian.

Apart from being used by the European Commission, SYSTRAN is also used by NATO and by Aérospatiale, the French aeronautic company, which has played an active part in the development of the system by contributing its own terminology bank for French-English and English- French translation and by financing the specialized area related to aviation. Outside Europe, SYSTRAN is used by The United States Air Force because of its interest in Russian-English translation, by the XEROX Corporation, which adopted machine translation at the end of the 1970s and which is the private company that has contributed the most to the expansion of machine translation, and General Motors, which through a license from Peter Toma is allowed to develop and sell the

31 applications of the system on its own account. It should be noted that in general the companies that develop direct machine translation systems do not claim that they are designed to produce good final translations, but rather to facilitate the translator's work in terms of efficiency and performance (Lab, p.24).

The transfer strategy focuses on the concept of "level of representation" and involves three stages. The analysis stage describes the source document linguistically and uses a source language dictionary. The transfer stage transforms the results of the analysis stage and establishes the linguistic and structural equivalents between the two languages. It uses a bilingual dictionary from source language to target language. The generation stage produces a document in the target language on the basis of the linguistic data of the source language by means of a target language dictionary.

The transfer strategy, developed by GETA (Groupe d'Etude pour la Traduction Automatique / Machine Translation Study Group) in Grenoble, France, led by B. Vauquois, has stimulated other research projects. Some, such as the Canadian TAUM-MÉTÉO and the American METAL, are already functioning. Others are still at the experimental stage, for example, SUSY in Germany and EUROTRA, which is a joint European project. TAUM, an acronym for Traduction Automatique de l'Université de Montréal (University of Montreal Machine Translation) was created by the Canadian Government in 1965. It has been functioning to translate weather forecasts from English to French since 1977 and from French to English since 1989. One of the oldest effective systems in existence, TAUM-MÉTÉO carries out both a syntactic and a semantic analysis and is 80% effective because weather forecasts are linguistically restricted and clearly defined. It works with only 1,500 lexical entries,

31 many of which are proper nouns. In short, it carries out limited repetitive tasks, translating texts that are highly specific, with a limited vocabulary (although it uses an exhaustive dictionary) and stereotyped syntax, and there is perfect correspondence from structure to structure.

The pivot language strategy is based on the idea of creating a representation of the text independent of any particular language. This representation functions as a neutral, universal central axis that is distinct from both the source language and the target language. In theory this method reduces the machine translation process to only two stages: analysis and generation. The analysis of the source text leads to a conceptual representation, the diverse components of which are matched by the generation module to their equivalents in the target language. The research on this strategy is related to artificial intelligence and the representation of knowledge. The systems based on the idea of a pivot language do not aim at direct translation, but rather reformulate the source text from the essential information. At the present time the transfer and pivot language strategies are generating the most research in the field of machine translation. With regard to the pivot language strategy, it is worth mentioning the Dutch DLT (Distributed Language Translation) project which ran from 1985 to 1990 and which used Esperanto as a pivot language in the translation of 12 European languages.

It should be repeated that unless the systems function within a rigidly defined sphere, as is the case with TAUM-MÉTÉO, machine translation in no way offers a finished product. As Christian Boitet, director of GETA (Grenoble) says in an interview given to the journal Le français dans le monde Nº314 in which he summarizes the most important aspects of MT, it allows translators to concentrate on producing a high-quality target text. Perhaps then "machine translation" is not an

32 appropriate term, since the machine only completes the first stage of the process. It would be more accurate to talk of a tool that aids the translation process, rather than an independent translation system.

The following is a relatively recent classification of some MT programs based on the results obtained from a series of tests that focused on errors and intelligibility in the target texts (Poudat, p.51).

2.7 Translation problems

2.7.1 General problems Translation is inherently a difficult activity. Translators can face additional problems which make the process even more difficult, such as:

 Problems with the source text:

 Changes made to the text during the translation process  Illegible text  Misspelled or misprinted text  Incomplete text  Poorly written text  Missing references in the text (for example the translator is to translate captions to missing photos)  The source text contains a translation of a quotation that was originally made in the target language, and the original text is unavailable, making word-for-word quoting nearly impossible  Obvious inaccuracies in the source text (for example "prehistoric Buddhist ruins", when Buddhism was not founded during prehistoric times)

 Language problems

33  Dialect terms and neologisms  Unexplained acronyms and abbreviations  Obscure jargon

 Other

 Rhymes, puns and poetic meters  Highly specific cultural references  Subtle but important properties of language such as euphony or dissonance

2.7.2 The problem of "un-translatability" The question of whether particular words are untranslatable is often debated, with lists of "untranslatable" words being produced from time to time. These lists often include words such as saudade, a Portuguese word as an example of an "untranslatable". It translates quite neatly however as "sorrowful longing", but does have some nuances that are hard to include in a translation; for instance, it is a positive-valued concept, a subtlety which is not clear in this basic translation.

Some words are hard to translate only if one wishes to remain in the same grammatical category. For example, it is hard to find a noun corresponding to the Russian почемучка (pochemuchka) or the Yiddish "shlimazl), but the English adjectives "inquisitive" and "jinxed) שלימזל correspond just fine.

Journalists are naturally enthusiastic when linguists document obscure words with local flavour, and are wont to declare them "untranslatable", but in reality these incredibly culture-laden terms are the easiest of all to translate, even more so than universal concepts such as "mother". This is because it is standard practice to translate these words by the same word

34 in the other language, borrowing it for the first time if necessary. For example, an English version of a menu in a French restaurant would rarely translate pâté de foie gras as "fat liver paste", although this is a good description. Instead, the accepted translation is simply pâté de foie gras, or, at most, foie gras pâté.

2.7.3 The problem of common words The words that are truly difficult to translate are often the small, common words. For example, the verb "to get" in all its various uses covers nearly seven columns of the most recent version of the Robert- Collins French-English dictionary. The same is true for most apparently simple, common words, such as "go" (seven columns), "come" (four and a half columns), and so forth.

Cultural aspects can complicate translation. Consider the example of a word like "bread". At first glance, it is a very simple word, referring in everyday use to just one thing, with obvious translations in other languages. But ask people from England, Sudan or other Arabic countries to describe or draw " and they will describe different things, based on their individual cultures.

Differing levels of precision inherent in a language also play a role. What does "there" mean? Even discounting idiomatic uses such as "there, there, don't cry", we can be confronted by several possibilities. If something is "there" but not very far away, a Sudanese will say it is further away he or she will say , unless there are connotations of "

", " " or " ", in which case the word is likely to be .

Conversely, in colloquial Arabic all three " " concepts plus the concept of "here" all tend to be expressed with the word

35 The problem often lies in failure to distinguish between translation and glossing. Glossing is what a glossary does: give a short (usually one-word) equivalent for each term. Translation, as explained above, is decoding meaning and intent at the text level (not the word level or even sentence level) and then re-encoding them in a target language. Words like run and wound are hard to "gloss" into a single other word, but given two or more words they can be perfectly adequately "translated". Similarly, depending on the context, the meaning of the French word "tutoyer", or Spanish "tutear", could be translated as "to be on first name terms with". "Bread" has perhaps a better claim to being untranslatable, since even if we resort to saying "French bread", "Chinese bread", "Algerian bread", etc. we are relying on our audience knowing what these are like.

2.7.4 Criticism of Machine Translation From time to time, criticism can be made of the act of translation. One such criticism is the lack of "coherence" in translation. The criticism can be stated as follows. If a story originally written in English, and taking place in an English speaking country, is translated into French, for example, it can lose its logic because of sentences like "Do you speak English?" The critic asks what the translation should be. "Parlez-vous anglais?" or "Parlez-vous français?". According to this criticism, the answer will be self-contradictory. If the answer to the question were yes, for the first translation this would mean something like, "Yes I speak a language you are not using and that is absolutely irrelevant". For the second translation it would mean "Yes, this is an English speaking country, and yet everyone, including myself, is speaking French." The gist of this criticism that one of the main rules in translation is to "keep

36 the context", and that the language of the document is itself the heart of the context.

This criticism can be rebutted in several ways. First, this kind of situation arises rarely in real-world translations. When it does, the translator can use techniques to avoid the problem by, for example, translating "Do you speak English?" by "Do you speak my language?" or "Do you understand what I say?" Another point is that a French-speaking reader who is reading a book written by, say, Agatha Christie describing a murder in an English stately home, most likely realises that the characters were speaking English in the original.

Another criticism is of a more philosophical nature. It claims that translation can be described as writing what you have read in another language. The question arises whether the reader can know whether the translator understands the original author perfectly. While this is the translator's job, it is the author who is praised for the work; but can a translation of Asimov be considered as Asimov's work? According to this criticism, translation could even be seen as "legal plagiarism". Translations can be quite different from the original: for instance, the name of Zaphod Beeblebrox in The Hitchhiker's Guide to the Galaxy by Douglas Adams was translated into French by Jean Bonnefoy as Zapi Bibici, and the name of Captain Widdershins in A Series of Unfortunate Events by Lemony Snicket was translated into Portuguese as Capitão Andarré. While this is not a huge difference, it is there. Adams and Snicket may not have been completely happy with this change, and it is by a series of such small changes that a translation becomes an adaptation, according to this criticism. There is the further consideration, that practically every name used in a fictional work is chosen by the author for some reason; this could be the mere sound of the name, or it

37 could involve some imbedded morpheme that evokes an associated sense. Therefore, since all languages have different phonologies, and different morphemes, we would fully expect that a fictional name be different in a translated work. Such justifications notwithstanding, real or perceived divergences between the source and translated texts is a long-time complaint of translation, that is expressed in the Italian expression Traduttore, traditore — every translation is a betrayal. On the other hand, rarely is a work of fiction translated without a negotiation as to rights, and many an author will be happy to put aside reservations about the names of characters for the opportunity to increase his readership.

The integration of MT services and computer-mediated communication tools like instant messaging (IM) allows people to communicate, at least in principle, with others who speak different languages while producing and receiving messages in their native language. However, as with second language use, MT imposes costs. Current MT services still sometimes produce erroneous translations or words unsuited to the communication context (e.g., by translating computer bug into the equivalent of computer insect), or by forming poor sentence compositions (e.g. by translating the Chinese sentence equivalent to ―Still need to confirm with the Shanghai side to see if there is enough time to make it,‖ into the English translation ―Need to keep Shanghai there confirms that I time‖). MT can make it difficult for group members to establish mutual knowledge or common ground, particularly when teams must refer to objects and entities in a workspace. As a result, studies have shown that when communication requires coming to agreement on objects of reference, using MT is less efficient than using a shared second language.

38 Previous comparisons of MT and second language use have focused primarily on overall outcomes of communication and collaborative work (e.g., agreeing on labels for objects). Less is known about which aspects of the communication process MT supports well or badly. For instance, MT might be differentially useful for message production versus comprehension. It may be easier for a native Mandarin speaker to produce than the English equivalent, but its translation could be very difficult to a native English speaking partner to understand. A second unanswered question concerns the possible benefits of using one’s native language in MT-mediated communication. Studies suggest native language use is more socially and cognitively advantageous than second language use. However, it is unknown if MT- enabled native language use will also work well, because the advantages of using one’s own native language use might be outweighed by the costs imposed by MT errors. In this research comparing MT-enabled versus English language communication in conversations between native-English speaking Americans and native Mandarin speaking Chinese nationals. We ask people to perform a brainstorming task with a partner who speaks a different native language. This domain allows us to examine (a) whether using MT to enable native language use benefits idea production and comprehension for non-native English speakers, and (b) whether MT affects communication behaviors when the language used is unchanged but incoming messages are mediated by MT. As we will show, MT influenced idea exchange for both Chinese and American participants. Allowing Chinese speakers to produce messages in their native language was indeed beneficial; they produced more new ideas when communicating in Chinese over MT than when using English.

39 In addition, although American participants used English and produced similar numbers of ideas in both the MT and English-only conditions, MT changed the way they communicated. Americans were less talkative, producing fewer conversational turns and words, when using MT than when using English as a common language. Finally, Americans and Chinese participants saw MT as less comprehensible than English- mediated conversation. These asymmetries between production and consumption, and between native and non-native speakers, advance current understandings of how MT affects teamwork and contribute to the design of cross-lingual communication that includes MT. Rather than seeing MT as categorically inferior to second language use, our results suggest that there are tradeoffs between MT-enabled and second language communication for certain processes and tasks. Understanding these tradeoffs can improve the design of CMC tools that leverage these asymmetries, interleaving MT and second language use to get the best of both worlds.

41 CHAPTER THREE METHODOLOGY 3.0 Introduction: This chapter is concerned with the methodology adopted to conduct the study: developing EFL learners Awareness about Machine Translation. It describes the sample involved in the study, tools of data collection and data analysis. 3.1 Sampling: 20 students from Gezira University – Faculty of Education – Hantoub are involved in this study by responding to a test. 3.2 Tools for collecting Data: A questionnaire has been adopted as a tool for collecting data. The practical reason for adopting such a tool is that the study sample is able to deal with it. Another reason is that the questionnaire investigates the different aspects of the study besides, it involves the essential thoughts that the researcher tires to consider. 3.2.1 Content of the Questionnaire: A questionnaire is used as a means of data collection; the questionnaire contains twenty questions, judged by faculty doctors, then examined the validity and reliability of test, it was calculated, then distributed among the sample and filled by the sample then discharged and analyzed with a statistical means (Statistical Packages for Social Studies, SPSS) 3. 2.2 Validity of the questionnaire: The questionnaire is said to be valid if it measures what it is intended to measure. Five lecturers judged the questionnaire and confirmed its validity. They conform every question separately. Based on their comments, the questionnaire was put in its final draft.

41 3.2.3 Reliability of the test: The reality of questionnaire has been checked by using the following equation:

x R y =    

Where R: reliability of the test N: number of all items in the test X: odd degrees Y: even degrees sum Reliability = (2*R) /(1+R) Val = reliability N = 20

x y = 9223

x y = 171402  x = 7573

 y = 11396

 (x) = 137641

( y) = 213444 After substitution the values on the previous equation which resulted that: 1- The correlation is equal to 0.92. 2- The reliability is 0.96 3- The validity is = 0.98 So, the questionnaire is reliable and valid.

42 3.3 Instrument for Data Analysis: Both percentage and frequency are used to analyze the responses of the respondents to the questionnaire. SPSS programme used to analyze the data. The following chapter will present the analysis, interpretation and discussion of the results of data collected.

43 CHAPTER FOUR DATA PRESENTATION AND ANALYSIS

4. 0 Introduction: This chapter will present the data and analysis of the results. 1.0 Data analysis and Dissection 4.1.1 for answering the question about the range of using machine translation in difficult words Statement Frequency Percent Agree 20 63 To some extent 10 25 Disagree 5 12 Total 40 100%

Figure (4.1.1)

70.0%

60.0%

50.0%

40.0%

62.5%

Percent 30.0%

20.0%

25.0% 10.0% 12.5% 0.0% Disagree To some extent Agree S1

Table (4.1.1) and the histogram explained that the majority of the sample disagree 62% that the range of using machine translation in difficult words

4.1.2: for answering the question of avoidance of using machine translation

Statement Frequency Percent Agree 30 75 To some extent 7 18 Disagree 3 8 Total 40 100%

80.0%

60.0%

40.0%

75.0% Percent

20.0%

17.5% 7.5% 0.0% Disagree To some extent Agree

S2 Table (4.1.2) and histogram indicates that the majority of the respondents agree 75% that the question of avoidance of using machine translation

4.1.3: for answering the question of using machine translation for translating texts Statement Frequency Percent Agree 21 52 To some extent 9 23 Disagree 10 25 Total 40 100%

60.0%

50.0%

40.0%

30.0%

52.5% Percent 20.0%

25.0% 10.0% 22.5%

0.0% Disagree To some extent Agree

Table (4.1.5) and figure (4.1.5) showsS3 that the majority of the respondents agree about difficulties in using machine translation in texts

4.1.4: for the encountering problem when using machine translation

Statement Frequency Percent Agree 25 63 To some extent 5 23 Disagree 10 25 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0%

25.0% 10.0% 12.5%

0.0%

The above mentioned table and figureDisagree explainedTo that some the extent majority of theAgree respondents agree 62.5% that there are many problemsS4 encountering problem when using machine translation

47 4.1.5: for answering the statement about the problems that encountered by Students when use machine translation

Statement Frequency Percent Agree 4 10 To some extent 25 63 Disagree 11 27 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0% 27.5% 10.0% 10.0% 0.0% Disagree To some extent Agree

The above mentioned table shows that the majority agree to some extent 62.5% that problems that encountered by Students when use machine translation

4.1.6 for answering the statement about the problems of using machine translation resulted from the progrmme

Statement Frequency Percent Agree 6 14 To some extent 9 23 Disagree 25 63 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0%

10.0% 22.5% 15.0%

0.0%

Disagree To some extent Agree

S6 The above mentioned table and figure shows that the majority of the sample disagree 63% that the problems the problems of using machine translation resulted from the programme.

49 4.1.7 for answering the statement about the range of machine translation is usefulness in translating any text and subject Statement Frequency Percent Agree 0 0 To some extent 0 0 Disagree 40 100 Total 40 100%

100.0%

80.0%

60.0%

100.0%

Percent 40.0%

20.0%

0.0%

Disagree

S7 Table (4.1.7) and the figure (4.1.7) indicates that, all the respondents disagree for the usefulness of machine translation

51 4.1.8 for answering the statement about the problem of using machine translation

Statement Frequency Percent Agree 35 88 To some extent 2 5 Disagree 3 7 Total 40 100%

100.0%

80.0%

60.0%

87.5% Percent 40.0%

20.0%

7.5% 0.0% 5.0% Disagree To some Agree extent S8 Table (4.1.8) and the figure (4.1.8) the majority of the respondents were disagree 62.5%

4.1.9 for answering the statement about the partiality of using Machine translation for certain subjects.

Statement Frequency Percent Agree 3 7 To some extent 1 3 Disagree 36 90 Total 40 100%

100.0%

80.0%

60.0%

90.0%

Percent 40.0%

20.0%

7.5% 0.0% 2.5% Disagree To some Agree extent

S9 The above mentioned table (4.1.9) explains the majority of the respondents agree 90% that the partiality of using Machine translation for certain subjects.

4.1.10 for answering the statement training can help to deal with information technology

Statement Frequency Percent Agree 9 23 To some extent 6 15 Disagree 25 63 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0%

10.0% 22.5% 15.0%

0.0% Disagree To some extent Agree

S10

The above mentioned table (4.1.11) shows that the majority agree to some extent 63% that training can help to deal with information technology

53 4.1.11 MT minimized resorting to the dictionary.

Statement Frequency Percent Agree 32 80 To some extent 5 12 Disagree 3 8 Total 40 100%

80.0%

60.0%

40.0% 80.0% Percent

20.0%

12.5% 7.5% 0.0% Disagree To some extent Agree S11

The above mentioned table and figure explains that the majority disagree 80% that the MT minimized resorting to the dictionary

54 4.1.12 Providing a glossary of words can overcome the difficulties in Shakespearean language.

Statement Frequency Percent Agree 10 25 To some extent 25 63 Disagree 5 12 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0%

25.0% 10.0% 12.5% 0.0% Disagree To some extent Agree

S12 Table (4.1.12) shows that the majority agrees to some extent 63% that providing a glossary of words can overcome the difficulties in Shakespearean language.

55 4.1.13: Mt is sometimes as step to translating a difficult text

Statement Frequency Percent Agree 1 3 To some extent 6 23 Disagree 30 75 Total 40 100%

80.0%

60.0%

40.0%

75.0% Percent

20.0%

22.5%

0.0% 2.5% Disagree To some extent Agree

S13

Table (4.1.13) explains that the majority of the respondents disagree 87% that Mt is sometimes as step to translating a difficult text

56 4.1.14 MT commits mistakes of word-order.

Statement Frequency Percent Agree 35 87 To some extent 3 8 Disagree 2 5 Total 40 100%

100.0%

80.0%

60.0%

87.5% Percent 40.0%

20.0%

7.5% 0.0% 5.0% Disagree To some Agree extent

S14 Table (4.1.14) indicates that the majority of the respondents agree to some extent 87% that MT commits mistakes of word-order.

4.1.15 MT encourages students

Statement Frequency Percent Agree 25 63 To some extent 11 27 Disagree 4 10 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0% 27.5% 10.0% 10.0% 0.0% Disagree To some extent Agree

S15 Table (4.1.15) explains that the majority of the respondents agree that MT problems encounter EFL students.

4.1.16 MT can give the gist( What it is about) of the source text.

Statement Frequency Percent Agree 12 30 To some extent 25 63 Disagree 3 07 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0% 30.0% 10.0% 7.5% 0.0% Disagree To some extent Agree

S16

Table (4.1.16) shows that the majority of the respondents agree 30%, whereas 63% are agree to some extent, and the rejected are only 7%. According to researcher this indicated the majority of the respondents are agree that MT can give the gist( What it is about) of the source text

59 4.1.17: Mt typically does involve human intervention, in the form of pre-editing and post-editing.

Statement Frequency Percent Agree 30 75 To some extent 4 10 Disagree 6 15 Total 40 100%

80.0%

60.0%

40.0%

75.0% Percent

20.0%

15.0% 10.0% 0.0%

Disagree To some extent Agree

S17 The above mentioned table and figure (4.1.17) reports that the majority of the respondents agree 75% that Mt typically does involve human intervention, in the form of pre-editing and post-editing.

4.1.18: human translation is better than machine translation

Statement Frequency Percent Agree 35 88 To some extent 3 8 Disagree 2 4 Total 40 100%

100.0%

80.0%

60.0%

87.5% Percent 40.0%

20.0%

7.5% 0.0% 5.0% Disagree To some Agree extent

S18

Table (4.1.18) explains that the majority of the respondents agree 88% that human translation is better than machine translation

4.1.19: is it certainly true even purely human-generated translations are prone to error

Statement Frequency Percent Agree 25 63 To some extent 6 15 Disagree 9 22 Total 40 100%

70.0%

60.0%

50.0%

40.0%

30.0% 62.5% Percent

20.0%

10.0% 22.5% 15.0%

0.0% Disagree To some extent Agree

The above mentioned table (4.1.19) showsS19 that the majority of the respondents agree 63%

4.1.20 do you think that MT is publicly available through tools on the internet such as Google translate, Babel fish

Statement Frequency Percent Agree 30 75 To some extent 6 15 Disagree 4 10 Total 40 100%

80.0%

60.0%

40.0%

75.0% Percent

20.0%

15.0% 10.0% 0.0% Disagree To some extent Agree S20

The above mentioned table (4.1.20) explains that the majority of the respondents agree 75% that MT is publicly available through tools on the internet such as Google translate, Babel fish

63 CHAPTER FIVE CONCLUSION, FINDINGS, AND RECOMMENDATIOINS

5.1 Introduction The chapter will be concerned with, conclusion, findings and recommendations of the study. 5.2 Conclusion MT a field of computational linguistics that investigates the translation of texts from one human language to another, which implies increasing interaction and the intertwining of different language communities. The study cast on solving the error of MT. M.T is not preferable for pedagogical purposes in general ,because to improve EFL learners performance, ideal translation that convoy's fidelity and transparency, is required. However, M.T can be used in education for improving slow learners disabilities in terms of using word by word, also it can use when needed as a last choice for interpretation texts. 5.3 Findings: 1.0Data analysis and Dissection 1. Using of Machine translation is different according to many aspects. 2. using machine translation create an avoidance of using machine translation for texts translation. 3. There are many problems encountering problem when using machine translation 4. The problems the problems of using machine translation resulted from the programme 5. the range of usefulness of machine translation is not valid when translating any text and subject

64 6. Machine translation can be used partially for the translation of certain subjects. 7. Training can help to deal with machine translation in translating some words. 8. MT minimized resorting to the dictionary 9. Providing a glossary of words can overcome the difficulties in Shakespearean language. 10. Mt is sometimes as step to translating a difficult text 11. MT commits mistakes of word-order 12. MT problems encounter EFL students. 13. MT can not give the gist( What it is about) of the source text 14. Mt typically does involve human intervention, in the form of pre- editing and post-editing. 15. Actually human translation is better than machine translation 16. MT is publicly available through tools on the internet such as Google translate, Babel fish

5.3 Recommendations: 1. A void using MT in translating long texts. 2. Revise all the materials if translated by MT. 3. Students should not depend completely on MT. 4. Designing a well belt infrastructure to cope with ICT development. 5. Enhance electronic systems to support translation progarmmes. 6. students should not use machine translation permanently 7. Teachers should be well trained. 8. MT spent more time in a mending and substituting suitable words 52% 9. students are not allowed to use MT in literary works

65 References:

Baker, M. 1992. In Other Words: A Course book on Translation. London: Sage Publication. Baker, M. 2004. Routledge Encyclopedia of Translation Studies. Shanghai: Shanghai Foreign Language Education Press. Bassnett, S. & Lefevere, A. 2001. Constructing Cultures - Essays on Literary Translation (M). Shanghai: Shanghai Foreign Language Education Press. Bhatia, N. (Ed.) 1992. The Oxford Companion to the English Language, pp. 1021-54. Billiani, F. (Ed.) 2007. Modes of Censorship and Translation: National Contexts and Diverse Media. Manchester: St. Jerome. Catford, J. C. A. 1965. Linguistic Theory of Translation. London: Oxford University Press. Cohen, J. M. 1986. Translation. Encyclopedia Americana, 27: 12. Colina, S. 2002. Second language acquisition, language teaching and translation studies. The Translator, 8/ 1: 1-24. Dimitriu, R. 2004. Omission in translation: perspectives. Studies in Translatology, 12/ 3. Douglas, B. H. 1980. Principles of Language Learning and Teaching. New Jersey: Prentice-Hall, Inc. Englewood Cliffs. Dryden, J. 1962. Of Dramatic Poesy and Other Critical Essays (2 Vols., Ed. Watson, G.). London & New York: Dent/ Dutton. Even-Zohar, I. 1990. Polysystem theory. Polysystem Studies, 11: 50-51. Hawkes, D. & Minford, J. 1973, 1977, 1980. The Story of the Stone (Vol. 1-5). London: Penguin Books. Hewson, L. & Martin, J. 1991. Redefining Translation: The Variational Approach. London: Routledge.

66 Hutchins, W. J. 2000. Early Years in Machine Translation: Memoirs and Biographies of Pioneers. Amsterdam: John Benjamins. Ivacovoni, A. 2009. Translation by omission. Retrieved from: http://iacovoni.wordpress.com/2009/02/01/translation-by-omission/. Kramsch, C. 2000. Language and Culture. Shanghai: Shanghai Foreign Language Education Press. . Kasparek, C. 1983. The translator's endless toil. The Polish Review, 28/ 2: 84. Lefevere, A. (Ed. & trans.) 1977. Translating Literature: The German Tradition from Luther to Rosenzweig. Assen, Netherlands: Van Gorcum. Lefevere, A. 2004. Translation, History, Culture: A Sourcebook. Shanghai: Shanghai Foreign Language Education Press. Mellinkoff, D. 1982. Legal Writing: Sense And Nonsense. St. Paul: West Publishing Co. Newmark, P. 2001. Approaches to Translation. Shanghai: Shanghai Foreign Language Education Press. Newmark, P. 2001. A Textbook of Translation. Shanghai: Shanghai Foreign Language Education Press. Ng, S. H. & Bradac, J. J. 1993. Power in Language: Verbal Communication And Social Influence. Newbury Park, CA: Sage. Nida, E. A. 2001. Language and Culture: Contexts in Translating. Shanghai: Shanghai Foreign Language Education Press. Nida, E. A. 2001. Toward a Science of Translating. Shanghai: Shanghai Foreign Language Education Press. Nida, E. A. & Taber, C. R. 2001. The Theory and Practice of Translation. Shanghai: Shanghai Foreign Language Education Press. Nord, C. 2001. Translating as a Purposeful Activity – Functionalist Approaches Explained. Shanghai: Shanghai Foreign Language Education Press.

67 Samovar, L. A. & Porter, R. E. 2000. Communication between Cultures. Beijing: Foreign Language Teaching and Research Press. Shuttleworth, M. & Cowie, M. 2004. Dictionary of Translation Studies. Shanghai: Shanghai Foreign Language Education Press. Snell-Hornby, M. 2001. Translation Studies: An Integrated Approach. Shanghai: Shanghai Foreign Language Education Press. 2001. Snell-Hornby , M. 2006. The Turns of Translation Studies: New Paradigms or Shifting Viewpoints? Philadelphia: John Benjamins. Tylor, D. 2005. Primitive Culture. Guangxi: Guangxi Normal University Press. Venuti, L. 1992. Rethinking Translation: Discourse, Subjectivity, Ideology. London & New York: Routledge. Venuti, L. 2004. The Translator’s Invisibility: A History of Translation. Shanghai: Shanghai Foreign Language Education Press. Vermeer, H. 1986. Pre-suppositions for a theory of translation. In Snell- Hornby (Ed.), Some Theoretical Considerations on Culture and Language. Wilson , A. 2009. Translators on Translating: Inside the Invisible Art. Vancouver: CCSP Press.

68 University of Gezira Faculty of Education, Hasaheisa Department of English Batch (10)

Questionnaire Dear teacher, I would be grateful to receive your responses to the following statements of the questionnaire which is intended to collect data for study under the title (Developing Students' Awareness about Translation (MT) Problems).Any information you give will be highly appreciated. Put (√) to explain your response. Statement Agree To some Disagree extent 1. Machine translation is valid for certain topics 2. MT needs enhancing 3. students should not use machine translation absolutely 4. students encounter difficulties when using MT 5. problems of using MT results from the programme 6. Machine translating needs training. 7. MT spent more time in a mending and substituting suitable words 8. students are not allowed to use MT in literary works 9. I do not agree to use MT at all 10. inability to translate accurately needs students to resort MT. 11. MT minimized resorting to the dictionary. 12. Providing a glossary of words can overcome the difficulties in Shakespearean language. 13. Mt is sometimes as step to translating a difficult text

69 14. MT commits mistakes of word-order. 15. MT encourages students 16. MT can give the gist( What it is about) of the source text. 17. Mt typically does involve human intervention, in the form of pre-editing and post-editing. 18. human translation is better than machine translation 19. is it certainly true even purely human-generated translations are prone to error 20. do you think that MT is publicly available through tools on the internet such as Google translate, Babel fish