Translation in Arabic language and literature
hossein jowkar; ali afzali; masoud fekri; shahriar niazi
Abstract
Several models have criticized translations, among which Garces' model includes more angles of translation criticism. This model is based on the principle of equality between the source and target text and examines the translation in four levels: semantic, syntactic, discourse and style, each level ...
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Several models have criticized translations, among which Garces' model includes more angles of translation criticism. This model is based on the principle of equality between the source and target text and examines the translation in four levels: semantic, syntactic, discourse and style, each level having its own related components. The present research aims to analyze the translation of Marashipour's novel "Bin-al-Qasrin" based on the semantic and syntactic levels of Garces model with the mixed content analysis method (quantitative-qualitative), and with the aim of evaluating the quality of the translation. In addition to the intrinsic value of the translation of "Bin-al-Qasrin", the reflection of the components of these two levels, has caused this translation to be examined by relying on the Garces model, which allows the evaluator to evaluate different components of these two. Apply the level to the translated text. So, the Garces model is explained in semantic and syntactic levels, and then the application of the components of these levels is mentioned on the examples. The result is that the translation can be criticized from the perspective of the components of the semantic and syntactic levels of the Garces model. And due to having positive features that are more numerous than negative features, this translation is sufficient and acceptable. Also, the translator has benefited from syntactic expansion, changing the syntax or grammar, changing the perspective or different perspective and removing more than the rest of the components.
Translation in Arabic language and literature
Shahryar Niazi; Mahmood Bijankhan; Mazyar Pashaei
Abstract
Abstract Man's need to translate with more efficiency has made him endeavor to achieve advanced translation technologies. Most of the efforts in this field have been devoted to achieving machine (automatic) translation (without human intervention), which, although it does not have the quality of human ...
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Abstract Man's need to translate with more efficiency has made him endeavor to achieve advanced translation technologies. Most of the efforts in this field have been devoted to achieving machine (automatic) translation (without human intervention), which, although it does not have the quality of human translation, has other advantages such as speed and high availability and low cost. The peak of these benefits can be seen in free online translation machines. Some of these machines (i.e. Google, Bing, Yandex, Reverso, ModernMT, and NiuTrans) support Arabic to Persian translation and vice versa. The purpose of this research is to compare the quality of Arabic<>Persian translations provided by these machines with each other. In order to achieve this goal, first, two small Arabic and Persian corpuses, each containing 60 sentences with random types and topics, were selected from the sentences in the two Arabic and Persian frequency dictionaries published by Routledge, then these sentences were entered one by one into the aforementioned translation machines. and the received output was scrutinized by human evaluation method based on the DQF-MQM error classification and analysis model. The translation machines in order from highest to lowest output quality are: Google, Bing, Yandex, ModernMT, Reverso, and NiuTrans. This is not an absolute and constant result, but a statistical and probabilistic one; lower-ranked machines translate some sentences better than the higher-ranked machines.Keywords: Translation Studies, Translation Technology, Machine Translation Evaluation, Google Translate, Bing Translator, Yandex Translate, Reverso, ModernMT, NiuTransIntroductionTranslation technology has been an important branch of translation studies. In 1972, at the third conference of applied linguistics, James Holmes introduced the field of translation technology as a sub-branch of the "applied" branch of the emerging interdisciplinary science of "translation studies". He divided this field into three categories: theories of translation by humans, by machines, and by both (Machine-Aided Human Translation or Human-Aided Machine Translation).Most of the efforts in the field of translation technology have been focused on making the machine able to translate without human intervention. This type of translation is called “machine translation”. Machine translation will not be able to beat professional human translation in the field of quality, but it has other advantages such as high speed, low cost and easy access.The pinnacle of convenient access and low cost for translation services can be seen in free online translation machines. They can be accessed and used for free through any system with a browser and connection to the Internet; Some also have a specific smartphone application that provides additional features such as offline translation.The evaluation of the phenomenon of machine translation generally includes many topics; Different aspects of it can be examined in different ways in response to the different needs of the people involved (including: end user, developer, and investor). Our focus in this article is on evaluating the quality of the output or product of the translation machines. The questions of the research are:1- Which free online translation machine do produce Arabic to Persian translation with better quality?2- Which free online translation machine do produce Persian to Arabic translation with better quality?A brief and widely used definition of “translated text quality” is as follows: “A quality translation demonstrates accuracy and fluency required for the audience and purpose and complies with all other specifications negotiated between the requester and provider, taking into account end-user needs”Literature ReviewSeveral scientific studies have dealt with the subject of comparative evaluation of machine translation for Arabic-English or Persian-English language pairs, but no research in this field has been published for Arabic-Persian language pairs. These researches have generally selected a test suite first, then translated it by several translation machines and studied the output using one or more special methods of machine translation evaluation. Here we present the summary of most recent researches. Ben Milad (2022), Almahasees (2020) and Al-Shalabi (2017) tested several machine translations between Arabic and English with different methods and all concluded that Google Translate produces better quality translations, just Abu-Ayyash (2017) concluded that Google Translate and Bing Translator produce similar quality outputs.Research MethodologyThere are various methods to evaluate machine translation quality. They are divided into two main subcategories of human and automatic evaluation. In this research we use a standard and up to date human evaluation model called DQF-MQM, and especially a subset of it that is appropriate for machine translation quality evaluation and is as follows: Four high-level error types of Accuracy, Fluency, Locale Convention, and Terminology. Accuracy type is further subdivided into four granular error types of Addition, Omission, Mistranslation, and Untranslated. Fluency type is further subdivided into three granular types of: Grammatical, Grammatical Register and Spelling.We used the excel template on the formal website of DQF-MQM to evaluate 60 Arabic-Persian and 60 Persian-Arabic translated sentences done by 6 online free translation machines that support Arabic<>Persian translation. The sentences were selected from two Arabic and Persian Frequency dictionaries and had random modes, genres and subjects.Conclusion
Translation in Arabic language and literature
Fatemeh Zarei; Ali Afzali; Shahriar Niazi
Abstract
One of the problems for translators is the adaptation to military ranks. The difficulty of a translator is doubled when the number of military ranks in the source and target languages is not the same; as a result, these terms remain unequal in one of the two languages, because there are differences in ...
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One of the problems for translators is the adaptation to military ranks. The difficulty of a translator is doubled when the number of military ranks in the source and target languages is not the same; as a result, these terms remain unequal in one of the two languages, because there are differences in the military structure of the armed forces of countries that ignoring them can create problems for translators. After describing Iran's military ranks and comparing it with Arab countries, the present study examines general and specialized dictionaries and investigates how to find their equivalence, using a descriptive-analytical and comparative method. The results indicate that most of the general dictionaries did not have sufficient knowledge of specialized fields and did not study the terms of this field carefully and specialized dictionaries could not be equivalent due to the fact that they were written by military people and had no information about the structure of Arab countries. The best way to choose an equivalent for the terms of military ranks is "equivalent selection after recognizing the pattern of military ranks of Iran and Arab countries.
Shahriar Niazi; Ensie Sadat Hashemi
Abstract
Evaluation of the Qur'an translation has not yet had a comprehensive model. To achieve such a model, we can first examine the efficacy of existing evaluation models. In this regard, the present article examines the effectiveness of the TT-oriented Garces model in evaluating the translation of the Qur'an, ...
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Evaluation of the Qur'an translation has not yet had a comprehensive model. To achieve such a model, we can first examine the efficacy of existing evaluation models. In this regard, the present article examines the effectiveness of the TT-oriented Garces model in evaluating the translation of the Qur'an, examining the “Syntactical-morphological” level. This level examines structural changes in translation. For this purpose, the translation of five Surahs of the Quran from the TT-oriented translation of Makarem Shirazi has been selected as the research data. Studying the efficiency of this level in evaluating the translation of the Qur'an also shows that the syntactic-lexical level of the Garces model is efficient in evaluating the translation of the Qur'an due to the structural differences between the Persian and Arabic languages and the necessity of syntactic changes in the translation of the Qur'an. However, the overall classification of techniques to the positives and negatives is should be reviewed according to their status and necessity. Among the techniques of this level, "Transposition", "Modulation" and "Explanation" have the most function in the Quran translation. According to the high usage of rhetorical devices in the Qur'an, the "Compensation" is an effective technique to evaluate the translator's skill in translating them to Persian. The "literal translation" and "Implication, reduction, omission" as negative techniques and "Changes in the type of sentences" as an ambivalent technique have had the least function in the Makarem translation as an example of a TT-oriented translation.
