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Journal articles on the topic 'Machine translation'
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1
He, Linli, Mozhgan Ghassemiazghandi, and Ilangko Subramaniam. "Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation." Forum for Linguistic Studies 6, no. 2 (April 22, 2024): 1189. http://dx.doi.org/10.59400/fls.v6i2.1189.
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This study explores the performance of machine translation of literary texts from English to Chinese. The study compares two machine translation systems, Bing Translator and Youdao Machine Translation, using selected texts from the novel “Nineteen eighty-four” by George Orwell. The data collection includes the original source texts, their machine-generated translations by Bing Translator and Youdao Machine Translation, and comparisons with human reference translations to assess the performance of these systems. The research’s focal point is to evaluate the accuracy, fluency, and appropriateness of translations generated by these two machine translation systems, while also analyzing the post-editing effort required to enhance the quality of the final machine-translated product. The study revealed that despite the presence of flaws in both machine translation systems, Youdao Machine Translation demonstrated superior performance, especially in accurately translating technical terms and idiomatic expressions, making it the more effective option overall. Nevertheless, the translations from Youdao Machine Translation required more substantial post-editing efforts to improve fluency and readability. Conversely, Bing Translator yielded more fluent and natural-sounding translations, albeit with a need for improved accuracy in translating technical terms and idiomatic expressions. The study concludes that while machine translation systems are capable of generating reasonable translations for literary texts, human post-editing remains essential to ensure the final output’s accuracy, fluency, and appropriateness. The study underscores the importance of selecting the appropriate machine translation system based on the nature of the text being translated. It also highlights the critical role of post-editing in refining the quality of machine-translated outputs, suggesting that while machine translation can provide a solid foundation, human intervention is indispensable for achieving optimal accuracy, fluency, and overall readability in literary translations.
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Ardi, Havid, Muhd Al Hafizh, Iftahur Rezqi, and Raihana Tuzzikriah. "CAN MACHINE TRANSLATIONS TRANSLATE HUMOROUS TEXTS?" Humanus 21, no. 1 (May 11, 2022): 99. http://dx.doi.org/10.24036/humanus.v21i1.115698.
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Machine translation (MT) have attracted many researchers’attention in various ways. Although the advanced of technology brings development to the result of MT, the quality are still criticized. One of the texts that has great challenges and translation problems is humorous text. Humorous texts that trigger a smile or laugh should have the same effect in another language. Humor uses linguistic, cultural, and universal aspects to create joke or humor. These raise questions how do machines translate humorous texts from English into Indonesian? This article aimed at comparing the translation result and error made by three prominent Machine Translations (Google Translate, Yandex Translate, and Bing Microsoft Translator) in translating humorous texts. This research applied qualitative descriptive method. The data were taken by comparing the translation results produced by 3 online Machine Translations in translating four humorous texts. The findings show that Google Translate produced better translation result. There are some errors related to lexical, syntaxis, semantics, and pragmatics errors in the. The implication of this finding shows that machine translation still need human in post editing to produce similar effect to preserve the humor.
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Xiu, Peng, and Liming Xeauyin. "Human translation vs machine translation: The practitioner phenomenology." Linguistics and Culture Review 2, no. 1 (May 9, 2018): 13–23. http://dx.doi.org/10.21744/lingcure.v2n1.8.
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The paper aimed at exploring the current phenomenon regarding human translation with machine translation. Human translation (HT), by definition, is when a human translator—rather than a machine—translate text. It's the oldest form of translation, relying on pure human intelligence to convert one way of saying things to another. The person who performs language translation. Learn more about using technology to reduce healthcare disparity. A person who performs language translation. The translation is necessary for the spread of information, knowledge, and ideas. It is absolutely necessary for effective and empathetic communication between different cultures. Translation, therefore, is critical for social harmony and peace. Only a human translation can tell the difference because the machine translator will just do the direct word to word translation. This is a hindrance to machines because they are not advanced to the level of rendering these nuances accurately, but they can only do word to word translations. There are different translation techniques, diverse theories about translation and eight different translation services types, including technical translation, judicial translation and certified translation. The translation is the process of translating the sequence of a messenger RNA (mRNA) molecule to a sequence of amino acids during protein synthesis. The genetic code describes the relationship between the sequence of base pairs in a gene and the corresponding amino acid sequence that it encodes.
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Garcia, Ignacio. "Is machine translation ready yet?" Target. International Journal of Translation Studies 22, no. 1 (June 30, 2010): 7–21. http://dx.doi.org/10.1075/target.22.1.02gar.
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The default option of the Google Translator Toolkit (GTT), released in June 2009, is to “pre-fill with machine translation” all segments for which a ‘no match’ has been returned by the memories, while the Settings window clearly advises that “[m]ost users should not modify this”. To confirm whether this approach indeed benefits translators and translation quality, we designed and performed tests whereby trainee translators used the GTT to translate passages from English into Chinese either entirely from the source text, or after seeding of empty segments by the Google Translate engine as recommended. The translations were timed, and their quality assessed by independent experienced markers following Australian NAATI test criteria. Our results show that, while time differences were not significant, the machine translation seeded passages were more favourably assessed by the markers in thirty three of fifty six cases. This indicates that, at least for certain tasks and language combinations—and against the received wisdom of translation professionals and translator trainers—translating by proofreading machine translation may be advantageous.
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Khabarova, E. M. "Machine translation of expressive means – metaphors." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (December 18, 2023): 108–19. http://dx.doi.org/10.17726/philit.2023.2.8.
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Technology has advanced significantly over the past decades. Significant changes have occurred in the field of translation with the development of programs such as Google.translate and Yandex.translator. The presented applications are already being actively implemented in translation agencies to optimize translation activities, where written translations of documents, articles, annotations, etc. must be provided to customers as quick as possible. While working with popular science text, online programs help translators gain time, but this requires to edit the text. The artistic style requires more concentration and dedication, because. the means of expression presented in it require taking into account the context and nuances of the use of certain units of language. Machine translation has the potential to become an indispensable assistant in the hands of a translator. This article discusses machine translation of expressive means, namely metaphors. The study is illustrated with examples and a comparative analysis of the translation of metaphorical units is carried out, the classification of metaphors is identified and an analysis is carried out with which means of expression the translator is able to cope with. The difficulties of translating metaphors are analyzed.
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Danylov, Hlib, Viktoriia Balakirieva, and Kateryna Vasylenko. "MACHINE TRANSLATION, MACHINE TRANSLATION SYSTEMS AND THEIR SPECIFICATIONS." Naukovy Visnyk of South Ukrainian National Pedagogical University named after K. D. Ushynsky: Linguistic Sciences 2021, no. 33 (December 2021): 293–310. http://dx.doi.org/10.24195/2616-5317-2021-33-22.
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The article is devoted to the study of machine translation, machine translation systems and their specificity. The relevance of the work is determined by fast and strong changes of auto-translators and sharply increasing, qualitatively changing needs. The aim of the study is to consider the translation of scientific and technical texts by machine translators without the participation of a professional translator. We use them in business correspondence, for informal communication with friends from other countries, on foreign trips. While having a number of advantages, they also have many disadvantages. The main problem raised in this article is whether a computer can completely replace a person. Answering this question, we come to the conclusion that such a replacement is impossible at the present stage of technology development. Machine translation is not yet capable of fully translating phraseological units and slang. The computer does not take into account the peculiarities of the context, the specifics of the construction of sentences, irony and humor. Only a person can convey all the nuances of the language, play on words, the author’s style. In some areas, even the most accurate and correct translation of a computer is subject to multiple human checks. This applies to the translation of medical topics, legal documents and texts, where the cost of an error can be very high, up to a human life. The same sad situation develops in the translation of works of art, in which, in addition to meaning, it is necessary to convey emotions, expression, imagery. In addition, the style of the work, culture, era, wordplay, humor should be preserved. Not every professional translator can do this. Even more difficult is the task of a translator working on a poetic form, where it is necessary to preserve both the meaning, and also the rhythm, tact, metaphor.
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Mohar, Tjaša, Sara Orthaber, and Tomaž Onič. "Machine Translated Atwood: Utopia or Dystopia?" ELOPE: English Language Overseas Perspectives and Enquiries 17, no. 1 (May 26, 2020): 125–41. http://dx.doi.org/10.4312/elope.17.1.125-141.
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Margaret Atwood’s masterful linguistic creativity exceeds the limits of ordinary discourse. Her elliptical language contributes to interpretative gaps, while the ambiguity and openness of her texts intentionally deceive the reader. The translator of Atwood’s texts therefore faces the challenge of identifying the rich interpretative potential of the original, as well as of preserving it in the target language. Witnessing the rise of artificial intelligence, a natural question arises whether a human translator could ever be replaced by a machine in translating such challenging texts. This article aims to contribute to the ongoing debate on literary machine translation by examining the translations of Atwood’s “Life Stories” generated by two neural machine translation (NMT) systems and comparing them to those produced by translation students. We deliberately chose a literary text where the aesthetic value depends mostly on the author’s personal style, and which we had presumed would be problematic to translate.
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Tahseen, Wesam Mohsen, and Shifa'a Hadi Hussein. "Investigating Machine Translation Errors in Rendering English Literary Texts into Arabic." Integrated Journal for Research in Arts and Humanities 4, no. 1 (January 18, 2024): 68–81. http://dx.doi.org/10.55544/ijrah.4.1.11.
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Machine translation is a machine that employs artificial intelligence (AI) to translate texts between languages without human intervention. Machine translation approaches translate text or speech from one language to another, including the contextual, idiomatic and pragmatic issues of both different languages. The present study aims to analyze the translation of literary texts selected from different novels, plays, and poems and clarify the method for translating them from English into Arabic. This study also aims to discover machine translation errors in rendering English literary texts and clarify the translator's role in transferring the rhetorical impact on the reader who reads the (TT). This study hypothesizes that translators(students) face difficulties regarding words and structures when translating literary texts from English into Arabic because they misunderstand rhetorical devices. So they tend to use machine translations that translate literally, such as (Google Translate, Reverso translation and Bing Microsoft translation). This study adopted two models: First, Newmark's translation model (1988b), which includes two basic types of translation: semantic and communicative. This model is used widely in the analysis of literary texts. Second, Harris (2018) linguistic model theory of rhetorical question and the general purpose of the rhetorical devices to analyze the data. Finally, the study ends with the conclusions that all machine translation programs (Google Translate (GT), Reverso Translation (Reverso. T), Bing Microsoft Translation (Bing. M.T) in rendering English literary texts from English into Arabic are unacceptable and have more problems because these programs are just machines and cannot think or feel as well as all these machines renderings are meaningless and ambiguous. So Human translation is better than Machine Translation because the first uses communicative translation while the other uses semantic translation.
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Abdullah H Aldawsar, Hamad. "Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms." Arab World English Journal 1, no. 1 (April 24, 2024): 237–51. http://dx.doi.org/10.24093/awej/chatgpt.16.
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This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.
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Zhang, Wanfang, and Yuan Tang. "Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment." Computational Intelligence and Neuroscience 2022 (August 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/7428563.
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With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People’s life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences.
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Rahul, Kodithala. "Neural Machine Translation." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 2027–30. http://dx.doi.org/10.22214/ijraset.2022.45669.
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Abstract: The project's novelty is not merely importing modules and preparing data and feeding the data to the model but understanding how the real language translation works and implementing the logics underlying each method utilized and creating every function from scratch, resulting in the creationof a Neural Machine Translation model. Initially, translation was accomplished by simply substituting words from one language for those from another. However, because languages are essentially different, a greater degree of knowledge (e.g., phrases/sentences) is required to achieveeffective results. With the introduction of deep learning, modern software now employs statisticaland neural techniques that have been shown to be more effective when translating. We are essentially translating German to English utilizing Sequence to Sequence models with attention and transformer models. Of course, everyone has access to Google Translates power, but if you want to learn how to implement translation in code, this project will show you how. We are writingour code from scratch, without using any libraries,in order to understand how each model works.While this design is a little out of date, it is still a great project to work on if you want to learn more about attention processes before moving on to Transformers. It is based on Effective Approaches to Attentionbased Neural Machine Translator and is a sequence to sequence (seq2seq) model for German to English translation.
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Halimah, Halimah. "COMPARISON OF HUMAN TRANSLATION WITH GOOGLE TRANSLATION OF IMPERATIVE SENTENCES IN PROCEDURES TEXT." BAHTERA : Jurnal Pendidikan Bahasa dan Sastra 17, no. 1 (January 31, 2018): 11–29. http://dx.doi.org/10.21009/bahtera.171.2.
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AbstractThis study aims to analyze the similarity between human translation and machine translation to translate procedural text. This research uses Content Analysis approach (Content Analysis). The analysis was performed on English procedural text on a "VIXAL Lebih Wangi" cleanliness product translated into Indonesian by Nia Kurniawati (representing human translation). Meanwhile Google translation is used to represent machine translation. The study of the equations compared in this study is from the aspect of the phrase and the meaning of the whole sentence in the results of the two translations. The result of the discussion shows that the equation between human translation and machine translation in translating procedural text is low, i.e 29%. Machine translation still requires manpower to produce better translations.
Keywords: equality aspect, human translation, machine translation, text procedure
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Fitria, Tira Nur. "A Review of Machine Translation Tools: The Translation’s Ability." Language Circle: Journal of Language and Literature 16, no. 1 (October 10, 2021): 162–76. http://dx.doi.org/10.15294/lc.v16i1.30961.
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The objective of the research is to review the ability of online machine translator tools includes Google Translate (GT), Collin Translator (CT), Bing Translator (BT), Yandex Translator (YT), Systran Translate (ST), and IBM Translator (IT). This research applies descriptive qualitative. The documentation was used in this study. The result of the analysis shows that the translation results are different, both from the style of language and the choice of words used by each machine translation tool. Thus, directly or indirectly, whether consciously or not, each translation machine carries its characteristics. Machine translation technology cannot be separated from the active role of humans. In other words, it will always be the best choice for users to rely on expert translation rather than machine translation. But no machine translator can be as accurate as human skills in producing translation products. In particular, the field of translation is also concerned with machine translation to support the performance of translators in analyzing the diction used as an element of language. In this regard, it needs to be underlined that the existence of machine translation is an additional facility in the world of translation, not as the main means of translation because the sophistication of the machine will not be able to match the flexibility of the human brain's cognitive abilities in adjusting the translation results according to the existing context. Accurate translation is sometimes subjective, relatively often temporal. Therefore, it is permissible for translating by more than one machine translator
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Sipayung, Kammer Tuahman. "The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan." Lingua Cultura 17, no. 2 (October 27, 2023): 153–59. http://dx.doi.org/10.21512/lc.v17i2.9971.
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The research aimed to describe the impact of machine translation on translation accuracy. Machine translation was widely used to translate the original language to the target. Accuracy was a crucial thing that the translator needed to restructure in the target language. The research applied a qualitative method with sampling based on its criteria. In addition, the research had two types of data: objective and effective. There were two instruments used to collect data; the first was instruction for translating a short film entitled “Roda Kehidupan”. The students were asked to translate a short film with the help of a machine, without machine translation, and the final version of the translation. The second instrument was the translation accuracy indicator, formulated in indicator form. The translation accuracy indicator (questionnaire) was distributed to inter-raters. The research shows that the accuracy of translation without machine translation (first version) is inaccurate (1,5); however, the accuracy of translation with machine translation (second version) is categorized as less accurate (2,4), and the translation accuracy on the final version of the translation is 2,3 (less accurate). The researcher suggests that the translator and lecturer need to use machine translation in translating, but a human touch (post-editing of translation) is really important to achieve high translation quality.
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Al-Shalabi, Riyad, Ghassan Kanaan, Huda Al-Sarhan, Alaa Drabsh, and Islam Al-Husban. "Evaluating Machine Translations from Arabic into English and Vice Versa." International Research Journal of Electronics and Computer Engineering 3, no. 2 (June 24, 2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.2.01.
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Abstract—Machine translation (MT) allows direct communication between two persons without the need for the third party or via dictionary in your pocket, which could bring significant and per formative improvement. Since most traditional translational way is a word-sensitive, it is very important to consider the word order in addition to word selection in the evaluation of any machine translation. To evaluate the MT performance, it is necessary to dynamically observe the translation in the machine translator tool according to word order, and word selection and furthermore the sentence length. However, applying a good evaluation with respect to all previous points is a very challenging issue. In this paper, we first summarize various approaches to evaluate machine translation. We propose a practical solution by selecting an appropriate powerful tool called iBLEU to evaluate the accuracy degree of famous MT tools (i.e. Google, Bing, Systranet and Babylon). Based on the solution structure, we further discuss the performance order for these tools in both directions Arabic to English and English to Arabic. After extensive testing, we can decide that any direction gives more accurate results in translation based on the selected machine translations MTs. Finally, we proved the choosing of Google as best system performance and Systranet as the worst one.
Index Terms: Machine Translation, MTs, Evaluation for Machine Translation, Google, Bing, Systranet and Babylon, Machine Translation tools, BLEU, iBLEU.
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Riyadh Rahim, Noor. "Google and Legal Translation: The Case Study of Contracts." Arab World English Journal For Translation and Literary Studies 8, no. 2 (May 26, 2024): 196–210. http://dx.doi.org/10.24093/awejtls/vol8no2.14.
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In recent years, the need for Machine Translation (MT) has grown, especially for translating legal contracts between languages like Arabic and English. This study primarily investigates whether Google Translator can adequately replace human translation for legal documents. Utilizing a widely popular free web-based tool, Google Translate, the research method involved translating six segments from various legal contracts into Arabic and assessing the translations for lexical and syntactic accuracy. The findings show that although Google Translate can quickly produce English-Arabic translations, it falls short compared to professional translators, especially with complex legal terms and syntax. Errors can be categorized into: polysemy, homonymy, legal doublets, and adverbs at the linguistic level, and morphological parsing, concord, and modality at the syntactic level. The study concludes with recommendations for enhancing machine translation systems and suggests caution in using Google Translate for legal purposes, advocating for continued reliance on human expertise in legal settings.
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Gusieva, Olena. "Automated translation and its “Post-machine” editing." Vìsnik Marìupolʹsʹkogo deržavnogo unìversitetu. Serìâ: Fìlologìâ 15, no. 26-27 (2022): 261–67. http://dx.doi.org/10.34079/2226-3055-2022-15-26-27-261-267.
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The article examines the peculiarities of text editing, in cases the translation is performed by using one of the automated translation systems. The relevance of the study is justified by the widespread use of automated translation programs, on the one hand, and the insufficient quality of machine translations, on the other. The introduction of automated systems into the practice of translation allows the analysis of machine translation texts to be included in the range of research tasks. In the applied aspect, there is a need to develop training courses that highlight the issue of machine translation. The purpose of the article is to define the nature of the tasks facing the editor of machine translation, and to develop general approaches to text editing. The task of the study is to analyze the journalistic text, to identify the nature of errors in the machine translation of phrases and sentences of the analyzed text, and to select methods for editing specific translation units. The objectives of the study also include the identification of typical errors of machine translation, followed by the definition of the post-editing algorithm, as well as the formulation of recommendations for machine translation editing. The study examined examples of machine translation, or rather examples of losses in translation from British English. It has been established that the most typical mistake in the translation of set phrases or set expressions is a word for word translation. The greatest difficulty for a machine translator is the translation of metaphorical expressions and phrases containing realities. At the same time, the degree of difficulty in translating a metaphor can be different. It was noted that post-editing is a mandatory element of working with the texts of machine translation. In automated translation, the machine acts as a supporter of word for word translation, and a human editor as a supporter of free translation. Post-editing of machine translation includes the ability to see logical errors and inconsistencies, the ability to select matches, to offer a descriptive translation, and, if necessary, to add a comment. During the analysis of automated translation, a certain dependence of the error rate on the characteristics of language units, namely, on the nature of lexical bindings, was revealed. In the course of the study, such correlations were established as typical characteristics of the text and the nature of automated translation errors; the nature of machine translation units and the degree of difficulty in editing. In general, it was concluded that the degree of participation of the translator or the linguist in editing the machine translation depends on the typical characteristics of the text.
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Bukhkalo, Svetlana, Anna Ageicheva, Anastasiia Vypovska, Zhanna Derkunska, and Nataliia Pshychkina. "STARTUP PROJECTS MACHINE TRANSLATION STRATEGY." Bulletin of the National Technical University "KhPI". Series: Innovation researches in students’ scientific work, no. 2 (December 29, 2021): 75–82. http://dx.doi.org/10.20998/2220-4784.2021.02.10.
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The importance of ICT usage implementation in the startup project is analyzed. It has been proved that translation is of great importance for any startup project for establishing relationships with potential clients around the world. The role of the translator in the startup project is investigated. Translation has been proven to be important for any startup project to build relationships with potential clients around the world. A comprehensive analysis of the translation of startup projects from Ukrainian into English using the latest information and communication technologies in this process. Peculiarities of using modern ICT in translating the description of startup projects from Ukrainian into English are revealed. Exploring the use of information and computer technology in the translation process. It is determined that it is important for a translator of a startup project to understand all the features of using the software, choose the appropriate programs or online tools and develop a strategy for the translation process in the project. The results of this work are very important and necessary for further study of the features of the use of ICT in the translation of startup projects
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Sokolova, Natalia. "Machine vs Human Translation in the Synergetic Translation Space." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije, no. 6 (February 2021): 89–98. http://dx.doi.org/10.15688/jvolsu2.2021.6.8.
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The paper focuses on English-to-Russian translations of patent applications on the website of the World Intellectual Property Organization (WIPO). A comparative analysis of patent applications is performed by using translations made with the help of the WIPO Translate tool and human translators within the framework of the synergetic translation space concept encompassing the domains of the author's intensions, text content and composition, energy, translator, recipient, and the translation acceptability notion. The translation erratology aspects were considered from the point of view of the semantic, referential, and syntactic ambiguity within the domains of content-composition and energy space. In the domain of the author, the intention to convey some technical information is revealed, while its rendering in the content-composition and energy domains depends on whether the translation is made by a person or a machine. Genre- and composition-related specifics have been rendered in both cases while machine translation errors have been proven to result from the semantic, referential, or syntactic ambiguity, and this is when the translated output is generally considered unacceptable by the recipient. The results obtained can be used for editing machine translations of patent documentation, assessing the quality of technical documentation translation that is referred to other specific genre conventions.
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Wang, Lan. "The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals." SHS Web of Conferences 163 (2023): 02021. http://dx.doi.org/10.1051/shsconf/202316302021.
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Machine translation, especially translation based on neural network technology has made a major breakthrough and is increasingly accepted and widely used. The development of artificial intelligence (AI) translation has had a definite impact on translation jobs. People, even professional translators, are relying on AI translation. But There is no research on whether machine translation software is superior to professional translators in translating various types of documents.. In this study, we design an experiment to determine the advantages and disadvantages between AI translations and human translations. The result shows the impact of the development of AI on the translation industry. To achieve better translation results and output highquality translations in the era of rapid development of AI, it will contribute to Human-AI partnerships.
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Purwaningsih, Dyah Raina, Ika Maratus Sholikhah, and Erna Wardani. "Revealing Translation Techniques Applied in the Translation of Batik Motif Names in See Instagram." Celt: A Journal of Culture, English Language Teaching & Literature 19, no. 2 (July 15, 2020): 287. http://dx.doi.org/10.24167/celt.v20i1.2090.
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This article discusses one of the forms of machine translation, the Instagram translation feature called “see translation”. The research is focused on the translation techniques applied by the machine in translating Banyumas batik motifs from Indonesian to English found in @batikantodjamil and @batk_rd. This topic is worth discussing since machine translation is now getting more developed and is projected to replace human translator. However, in some cases, for example in dealing with culturally-bound terms, machine translation cannot perform contextual knowledge as well as the human translator. this mini research was conducted by applying qualitative research with purposive sampling technique in which the researchers obtain the data by selecting two batik center Instagram accounts containing batik motif names in the captions. The result shows that there are three translation techniques applied by the Instagram translation features, namely literal, borrowing, and particularization. The most dominant technique to use is borrowing technique, and it shows a tendency that such cultural terms in the source language do not have one-to-one correspondence in the target language. In other words, the touch of human translator is very important in the post-editing process of translation by machine to make the translation more acceptable. However, if it is impossible to involve human translator, the Instagram administrator should enrich the machine with more contextual linguistic database to provide the users with better translation results.
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Purwaningsih, Dyah Raina, Ika Maratus Sholikhah, and Erna Wardani. "Revealing Translation Techniques Applied in the Translation of Batik Motif Names in See Instagram." Celt: A Journal of Culture, English Language Teaching & Literature 19, no. 2 (May 27, 2021): 287. http://dx.doi.org/10.24167/celt.v19i2.2090.
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This article discusses one of the forms of machine translation, the Instagram translation feature called “see translation”. The research is focused on the translation techniques applied by the machine in translating Banyumas batik motifs from Indonesian to English found in @batikantodjamil and @batk_rd. This topic is worth discussing since machine translation is now getting more developed and is projected to replace human translator. However, in some cases, for example in dealing with culturally-bound terms, machine translation cannot perform contextual knowledge as well as the human translator. this mini research was conducted by applying qualitative research with purposive sampling technique in which the researchers obtain the data by selecting two batik center Instagram accounts containing batik motif names in the captions. The result shows that there are three translation techniques applied by the Instagram translation features, namely literal, borrowing, and particularization. The most dominant technique to use is borrowing technique, and it shows a tendency that such cultural terms in the source language do not have one-to-one correspondence in the target language. In other words, the touch of human translator is very important in the post-editing process of translation by machine to make the translation more acceptable. However, if it is impossible to involve human translator, the Instagram administrator should enrich the machine with more contextual linguistic database to provide the users with better translation results.
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Park, Sue Jin. "L2 Writing in a Machine Translation-based Korean Writing Class －Learner Perceptions and Characteristics of Translated Texts." Korean Association of General Education 17, no. 3 (June 30, 2023): 139–53. http://dx.doi.org/10.46392/kjge.2023.17.3.139.
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This study examined how Korean learners perceive machine translation in Korean writing class, what the characteristics of machine-translated texts are, and what patterns appear depending on the level of Korean proficiency. Based on these results, this study aimed to suggest how machine translation in Korean writing class would help both of instructors and students. According to a survey of 77 Korean learners, 96% use machine translation and about 90% find it convenient. For beginners, most used machine translation when translating their native language into Korean, while intermediate and advanced learners used machine translation when translating Korean into their native language. Machine translation was mainly used for learning written language. In the second survey of same population, more than 98% of learners recognized that machine translation was convenient but inaccurate, and 97% required that there would be activities to use machine translation which could also provide feedback during class time. In sum, advanced level learners reviewed and modified machine-translated results more carefully than beginners and intermediate level learners, while beginners reviewed and modified less carefully than intermediate and advanced level learners. Thus based on this study, the teaching and learning methods for using machine translation in the writing class were presented as ‘1) finding problems and correcting one’s own language knowledge through self-correction after using machine translation, 2) discovering the differences between one’s mother tongue and Korean through back-translation activities, 3) discovering and using ways to reduce machine translation errors, where 4) the instructor should guide learners to discover cultural elements and provide explicit feedback., discovering various translations according to translation purpose and intention through cooperative activities.’
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Antika, Ria. "TRANSLATION SHIFTS AND EQUIVALENCE STRATEGY PRODUCED BY INSTAGRAM MACHINE TRANSLATION." JIPIS 31, no. 1 (April 6, 2022): 63–73. http://dx.doi.org/10.33592/jipis.v31i1.2136.
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The fast advancement of technology forces people to use it to simplify their lives. In the translation field, besides human translator, there is also machine translator used to help people in transferring the meaning of a text from source language (SL) to target language (TL). Along with the development of technology and social media, Instagram as one of the most famous social media platforms launched Instagram Machine Translation in 2016 that can help people to translate the captions and comments posted by the users. This study was conducted to identify the types of translation shift and the types of translation equivalence performed by Instagram machine translation in translating the captions on @instagram account from English to Indonesian. By using descriptive qualitative method to reach the goals of the study, the appropriate data were obtained. The data were analyzed based on the translation shifts theory from Catford (1965) and translation equivalence by Nida (1964). The findings showed that all types of Catford’s translation shifts were found in the translation process and the most common type was structure shift. It was also found that Instagram machine translation was successful in transferring the message from SL to TL equivalently.
Keywords: Translation shift, Translation equivalence, Instagram Machine Translation
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M. AI-Salman, Saleh. "The Effectiveness of Machine Translation." International Journal of Arabic-English Studies 5, no. 1 (January 1, 2004): 145–60. http://dx.doi.org/10.33806/ijaes2000.5.1.8.
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Insofar as machine translation is based on computerized natural language processing techniques, it still subscribes to the popular notion that the best translations are not simple word-for-word translations. Consequently, approaches to translation both by humans as well as machines face the same difficulties. The need for analyzing structural similarities between natural languages (e.g., English and Arabic), going beyond the surface structure to analyze the core meaning and translate concepts into other languages , among other things, still holds This paper maps out the pros and cons of machine translation in dealing with problems of contextuality, culture-bound expressions, lexical and structural ambiguity, and idiomatic expressions. The paper concludes that while considering machine translation a step in the right direction, it is premature to announce the birth of a full-fledged and independent approach to translation which can replace human translators . Even by capturing word expressions and building a database of translation phrases, computers cannot perform so well as human translators in most types of translation, despite the computer's ability to save time, cost and effort..
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Wolk, Krzysztof, and Krzysztof P. Marasek. "Translation of Medical Texts using Neural Networks." International Journal of Reliable and Quality E-Healthcare 5, no. 4 (October 2016): 51–66. http://dx.doi.org/10.4018/ijrqeh.2016100104.
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The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.
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Qizi, Yunusova Nilufar Maxmudjon. "Productivity of Machine Translation." International Journal of Teaching, Learning and Education 2, no. 1 (2023): 01–02. http://dx.doi.org/10.22161/ijtle.2.1.1.
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This article is about the field of technical translation which is called machine translation (MT), or machine-assisted translation. This method of translation uses various types of computer software to generate translations from a source language to a target language without the assistance of a human. There are different methods of machine translation. A plethora of machine translators in the form of free search engines are available online. However, within the field of technical communication, there are two basic types of machine translators, which are able to translate massive amounts of text at a time. There are transfer-based and data-driven machine translators. Transfer-based machine translation systems, which are quite costly to develop, are built by linguists who determine the grammar rules for the source and target languages. The machine works within the rules and guidelines developed by the linguist. Due to the nature of developing rules for the system, this can be very time-consuming and requires an extensive knowledge base about the structures of the languages on the part of the linguist; nonetheless, the majority of commercial machine translators are transfer-based machines. .
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Razsiah, Fadel, Ahmat Josi, and Sari Mubaroh. "Aplikasi Penerjemah Bahasa Bangka Ke Bahasa Indonesia Menggunakan Neural Machine Translation Berbasis Website." Jurnal Inovasi Teknologi Terapan 1, no. 1 (February 2, 2023): 68–76. http://dx.doi.org/10.33504/jitt.v1i1.67.
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At this time, machine translation is one of the options for translating languages, especially for people who want to learn a language. Bangka language is a language that is often used by the Bangka people for everyday life. There have been many developments for machine translation, but no one has yet developed a translation machine for the Bangka language. The development of a machine translation using Neural Machine Translation (NMT) and its implementation using the Flask microframework is the first step for the development of a machine translation for the Bangka language. This study aims to make and find out the translation results of the Bangka language translator application into Indonesian using the RNN translator model. The development starts from creating a parallel corpus of Bangka language to Indonesian and machine translation architecture with the RNN model. The research method starts from dataset collection, data preprocessing, modeling and training, system evaluation and implementation. From the results of the BLEU Scores evaluation, a value of 55.3% was obtained for Bangka to Indonesian. Furthermore, the model is implemented in the form of a website by utilizing the Flask framework, so that users can easily translate.
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Toral, Antonio, and Andy Way. "Machine-assisted translation of literary text." Culture & Society issue 4, no. 2 (December 31, 2015): 240–67. http://dx.doi.org/10.1075/ts.4.2.04tor.
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Contrary to perceived wisdom, we explore the role of machine translation (MT) in assisting with the translation of literary texts, considering both its limitations and its potential. Our motivations to explore this subject are twofold, arising from: (1) recent research advances in MT, and (2) the recent emergence of the ebook, which together allow us for the first time to build literature-specific MT systems by training statistical MT models on novels and their professional translations. A key challenge in literary translation is that one needs to preserve not only the meaning (as in other domains such as technical translation) but also the reading experience, so a literary translator needs to carefully select from the possible translation options. We explore the role of translation options in literary translation, especially in the context of the relatedness of the languages involved. We take Camus’ L’Étranger in the original French language and provide qualitative and quantitative analyses for its translations into English (a less-related language) and Italian (more closely related). Unsurprisingly, the MT output for Italian seems more straightforward to be post-edited. We also show that the performance of MT has improved over the last two years for this particular book, and that the applicability of MT does not only depend on the text to be translated but also on the type of translation that we are trying to produce. We then translate a novel from Spanish-to-Catalan with a literature-specific MT system. We assess the potential of this approach by discussing the translation quality of several representative passages.
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Rudianto, Gaguk, and Zia Hisni Mubarak. "TRANSLATION ANALYSIS OF TRANSLATING MACHINE." TONIL: Jurnal Kajian Sastra, Teater dan Sinema 19, no. 2 (December 17, 2022): 82–89. http://dx.doi.org/10.24821/tnl.v19i2.8169.
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Fenomena penerjemahan mempunya dampak yang sangat besar dalam kehidupan sehari-hari. Hal ini bisa dilihat dari hamper semua aspek aktivitas sangat dipengaruhi oleh penerjemahan. Contoh yang paling sederharna adalah dari penggunaan gawai Ketika seseorang memainkan game atau menggunakannya sebagai alat untuk berkomunikasi dengan orang lain. Dalam hal ini baik disengaja maupun tidak dia telah menggunakan jasa penerjemah yang telah menerjemahkan buku panduan dari penggunaan gawai tersebut dari Bahasa sumber dari produsen gawai ke Bahasa sasaran dari penggunanya. Tanpa buku panduan yang telah diterjemahkan ke Bahasa sasaran pelanggannya (TT), pengguna gawai tidak akan dapat menggunakannya dengan benar. Pengaruh penerjemagan secara besar-besaran juga dapat dilihat ketika seseorang pemeluk agama beribadah dimana sumberberasal dari nya kitab suci yang sudah penerjemahan Dalam menerjemahkan dokumen, ada beberapa prosedur yang harus delakukan (Venuti, 2021). Seperti 1) peminjaman, 2) Calque, 3) Penerjemahan perkata 4) Transposisi, 5) Modulasi 6) Equivalensi dan 7) Adaptasi. Artikel ini menganalisa prosedur penerjemahan dari pidato Presiden Joko Widodo dalam Rapat Umum Perserikatan Bangsa Bangsa yang ke 76. Hasil dari Analisa tersebut dapat ditemukan sebagai berikut proses borrowing terdiri dari 34 kata, Calque terdiri dari 7 ungkapan, Literal Translation mempunyai 4 kalimat, Transposition terdiri dari 11 kata, Modulation mempunyai 7 kata dan Equivalence mempunyai 2 katas. Metode pengumpulan data yang digunakan dalam artikel ini adalah non participatory observation dimana peneliti melakukan observasi tidak langsung yaitu dengan cara menonton video tersebut dan beberapa dokumen dan catatan dengan cara membandingkan pidato secara oral dan dibandingkan dengan teks berjalan yang ada di bagian bawah dari pidato tersebut. Sementara itu metode Analisa data yang digunakan adalah categorisasi berdasarkan teori prosedur penerjemahan dari Vinay and Darlbelnet. Kata kunci: Prosedur penerjemahan, teks sasaran, rapat umum, teks berjalan.
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Kaka-Khan, Kanaan Mikael, and Fatima Jalal Taher. "Evaluation of inkurdish Machine Translation System." Journal of University of Human Development 3, no. 2 (June 30, 2017): 862. http://dx.doi.org/10.21928/juhd.v3n2y2017.pp862-868.
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Lack of having a perfect machine translation for Kurdish language is a huge gap in Kurdish Language processing (KNLP). inkurdish is a first machine translation system for Kurdish language which is capable of translating English into Kurdish sentences. Building "inkurdish" machine translation system was a great point regarding Kurdish language processing, but like any other translation system has strengths as well as many shortcomings and issues. This paper tries to evaluate inkurdish machine translation system according to both linguistics and computational issues. It might help any other researchers interested in doing research in this field. It attempts to evaluate inKurdish from different perspectives, such as, giving un common words, sentences, phrases and paragraphs in this machine to check whether it provides the correct translation or not. A general evaluation can be done after getting a valid sample with their translations from the machine and compared to the meanings of the words outside the machine.
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Munkova, Dasa, Michal Munk, Ľubomír Benko, and Petr Hajek. "The role of automated evaluation techniques in online professional translator training." PeerJ Computer Science 7 (October 4, 2021): e706. http://dx.doi.org/10.7717/peerj-cs.706.
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The rapid technologisation of translation has influenced the translation industry’s direction towards machine translation, post-editing, subtitling services and video content translation. Besides, the pandemic situation associated with COVID-19 has rapidly increased the transfer of business and education to the virtual world. This situation has motivated us not only to look for new approaches to online translator training, which requires a different method than learning foreign languages but in particular to look for new approaches to assess translator performance within online educational environments. Translation quality assessment is a key task, as the concept of quality is closely linked to the concept of optimization. Automatic metrics are very good indicators of quality, but they do not provide sufficient and detailed linguistic information about translations or post-edited machine translations. However, using their residuals, we can identify the segments with the largest distances between the post-edited machine translations and machine translations, which allow us to focus on a more detailed textual analysis of suspicious segments. We introduce a unique online teaching and learning system, which is specifically “tailored” for online translators’ training and subsequently we focus on a new approach to assess translators’ competences using evaluation techniques—the metrics of automatic evaluation and their residuals. We show that the residuals of the metrics of accuracy (BLEU_n) and error rate (PER, WER, TER, CDER, and HTER) for machine translation post-editing are valid for translator assessment. Using the residuals of the metrics of accuracy and error rate, we can identify errors in post-editing (critical, major, and minor) and subsequently utilize them in more detailed linguistic analysis.
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Benyahia, Moad. "Examining the Efficiency of Machine Translation in Translating English Idioms used in American Media." Journal of Translation and Language Studies 5, no. 2 (June 2, 2024): 43–55. http://dx.doi.org/10.48185/jtls.v5i2.1070.
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The use of idioms permeates the American media, be it movies, series, TV sitcoms, chat shows, radio chats, documentaries and so forth. By definition, idioms are fixed expressions whose meaning cannot be inferred merely from the meanings of the words that compose it. With this in mind, translators face a tremendous challenge when attempting to translate American media to another language, whether that was done through dubbing or via the use of subtitles. Nevertheless, today’s available resources can provide an appreciable assistance regarding such challenges. Among these resources are online machine translation services, particularly the ones that are based on machine learning and artificial intelligence. The central aim of this study was to explore the efficiency of online machine translation services in translating idioms used in American media. To attain this aim, three of the most reliable online machine translation services were examined to determine their level of accuracy concerning idioms translation: Google Translate, DeepL Translator, and Bing Microsoft Translator. The results have indicated that machine translation is capable of translating idioms with an average accuracy rate of 68.7%, with Bing Microsoft Translator being the most accurate. In most cases, the translation is done by paraphrasing. However, Bing Microsoft Translator sometimes opts for using idioms in the target language that are similar in terms of meaning to the ones in the source language, which reveals new horizons for the development of high-tech online machine translation services that would be able to accurately and meaningfully translate cultural-specific lexis such as idioms.
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Tîrban, Emilian. "On the Efficiency and Efficacy of Machine-Assisted Literary Translation: A Case Study for English/Romanian and Romanian/English Machine-Assisted Translation." East-West Cultural Passage 23, no. 2 (December 1, 2023): 59–79. http://dx.doi.org/10.2478/ewcp-2023-0013.
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Abstract “If you translate long into the machine, the machine translates back into you,” is one of the issues the present article strives to establish and explore qualitatively. I intend to examine the effectiveness and efficiency of machine-assisted translations of significant literary works from a hermeneutical perspective. Essentially, I analyse the output of automated translation platforms such as Google Translate and compare them to human translation. This investigation is valuable in determining whether translators should exercise caution when utilizing translation platforms for culturally rich literary works. Additionally, the article scrutinizes the localisation, cultural, and grammatical coherence of Homer’s The Iliad translated from English to Romanian using the Google Translate platform. The human translations used are rendered into English and Romanian from Greek. As Homer’s Greek remains incomprehensible to the translation platform, we employ a secondary translation technique for a tertiary machine-assisted output. Nonetheless, this approach highlights the serious pitfalls of using translation platforms haphazardly in translation work. This analysis will show how awareness of the machine’s imperfect translation capabilities may, in turn, enhance the human translator’s awareness of what works while translating with the help of a translation application.
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Zeghbib, Ghaniyya. "Machine translation; Prospects and challenges." Mathematical Linguistics 2, no. 1 (December 31, 2022): 86–99. http://dx.doi.org/10.58205/ml.v2i1.154.
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Machine translation nowadays plays the role of mediating between languages in the transfer of scientific and cultural production between different countries of the world in order to ensure easy and comprehensive scientific communication, as it has had great credit in translating many scientific texts, and achieved positive results besides human beings in various fields, but the latter suffers from problems and difficulties which led to turning a blind eye to them by users.
Machine translation is still questionable because it is unable to convey many sentences and ideas that are alien to them, since the machine is limited and does not have another system outside the programmed language, it does a literal translation, which calls for the intervention of the human factor or, rather, the translator who covers that deficit by rewriting and modifying the text.
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Abadou, Fadila, and Saleh Khadich. "Coherence in Machine Translation Output." Traduction et Langues 18, no. 2 (December 31, 2019): 138–53. http://dx.doi.org/10.52919/translang.v18i2.425.
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Coherence is a cognitive process. It plays a key role in argumentation and thematic progression. To be characterised by appropriate coherence relations and structured in a logical manner, coherent discourse/text should have a context and a focus. However, it receives little attention in Machine translation systems that considers the sentence the largest translation unit to deal with, the fact that excludes the context that helps in interpreting the meaning (either by human or automatic translator). In addition to that, Current MT systems suffer from a lack of linguistic information at various stages (modelling, decoding, pruning) causing the lack of coherence in the output. The present research aims at, first, capturing the different aspects of coherence, and second, introducing this notion in texts generated by machine translation based on sentence-by-sentence basis, in order to see and discuss the several phenomena that can lead to incoherent document translations with different language pairs.
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Screen, Benjamin. "Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation." Studia Celtica Posnaniensia 2, no. 119 (September 26, 2017): 142–24. http://dx.doi.org/10.1515/scp-2017-0007.
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AbstractThis article reports on a controlled study carried out to examine the possible benefits of editing Machine Translation and Translation Memory outputs when translating from English to Welsh. Using software capable of timing the translation process per segment, 8 professional translators each translated 75 sentences of differing match percentage, and post- edited a further 25 segments of Machine Translation. Basing the final analysis on 800 sentences and 17,440 words, the use of Fuzzy Matches in the 70-99% match range, Exact Matches and Statistical Machine Translation was found to significantly speed up the translation process. Significant correlations were also found between the processing time data of Exact Matches and Machine Translation post-editing, rather than between Fuzzy Matches and Machine Translation as expected. Two experienced translators were then asked to rate all translations for fidelity, grammaticality and style, whereby it was found that the use of translation technology either did not negatively affect translation quality compared to manual translation, or its use actually improved final quality in some cases. As well as confirming the findings of research in relation to translation technology, these findings also contradict supposed similarities between translation quality in terms of style and post-editing Machine Translation.
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Zuhairo, Zuhairo, and Farida Repelita Waty Kembaren. "Intermediate Students' Perceptions of the Transformation of Online Translation Engine." IJLECR - INTERNATIONAL JOURNAL OF LANGUAGE EDUCATION AND CULTURE REVIEW 10, no. 1 (June 20, 2024): 12–20. http://dx.doi.org/10.21009/ijlecr.v10i1.45004.
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Online language translation machine is a tool that is currently popular to be used as a benchmark when translating words and sentences from foreign languages that are widely used by people today, especially students and students who can change the way of learning foreign languages by facilitating the translation process. Currently, online translation machines are really increasing and varying from those available on the website to having their own platforms such as special applications for language translation machines, so this research appears to analyse online translation machines according to students who have used them, this research method is a qualitative method conducted through a platform using interviews that see from the perception or point of view of users, namely high school students. The results of the interviews found that out of 13 students have almost the same point of view but with a variety of different reasons, most of them say that the translation machine is growing rapidly and is increasingly sophisticated as evidenced by the existence of new and interesting features that make it easier for them to translate various languages. It can be concluded that their perception of the online translation machine is interesting and quite realistic that the current online translation machine is indeed growing quite rapidly starting from the features and translation accuracy that continues to increase, it will be sure for the future online translation machines will be more sophisticated and become the main tool in translating various languages.
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AbuSa'aleek, Atef Odeh. "The Adequacy and Acceptability of Machine Translation in Translating the Islamic Texts." International Journal of English Linguistics 6, no. 3 (May 26, 2016): 185. http://dx.doi.org/10.5539/ijel.v6n3p185.
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<p>Islamic translation is considered as a special distinguished sub-discipline of applied linguistics. It is one of the most important areas of translation because it carries the values and eternal message. Through the history, the first translation work was of religious books. This study attempts to evaluate the adequacy and acceptability of four machine translation (MT) systems (World lingo, Babylon translation, Google translate, Bing translator) in translating the Islamic texts. In addition, it aims to evaluate the Islamic translation outputs based on functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability, and fidelity). The findings indicted that Google Translate System is the most adequate and acceptable among the other three systems (World lingo, Babylon translation, Bing translator) in translating the Islamic texts. The findings also revealed that Google Translate is acceptable in producing Islamic translation outputs in regard to the following functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability and fidelity) due to Google Translate advancement.</p><strong></strong>
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Al-Kaabi, Mouza H., Naji M. AlQbailat, Amjad Badah, Islam A. Ismail, and Khalid B. Hicham. "Examining the Cultural Connotations in Human and Machine Translations: A Corpus Study of Naguib Mahfouz's Zuqāq al-Midaqq." Journal of Language Teaching and Research 15, no. 3 (May 8, 2024): 707–18. http://dx.doi.org/10.17507/jltr.1503.03.
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The translation of culture-specific terms constitutes a major challenge for professional translators as it necessitates a thorough understanding of both the linguistic and cultural elements. With rapid technological advancement over the past few years, machine translation has enhanced translation quality. This study investigates the transability of cultural connotations in the Arabic-English translation of Naguib Mahfouz’s Zuqāq al-Midaqq. A descriptive qualitative research design was adopted to achieve the intended goals of the study. The data comprised human and machine translations from Google Translate and ChatGPT. Through qualitative content analysis, the translations were compared for accuracy in transferring the cultural connotations prevalent in the Arabic source text. The findings revealed that the human translation showed greater naturalness and accuracy in rendering cultural connotations. Machine translation has struggled with rhetorical devices, idioms, and cultural nuances. The results also indicated that the AI-enhanced machine represented by ChatGPT captured the cultural elements more effectively than Google Translate. The study concluded that human expertise remains essential for the high-quality translation of literary works to maintain cultural significance. The findings can inform translator training and guide improvements to AI-enhanced translation for literary texts.
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Luo, Jinru, and Dechao Li. "Universals in machine translation?" International Journal of Corpus Linguistics 27, no. 1 (February 14, 2022): 31–58. http://dx.doi.org/10.1075/ijcl.19127.luo.
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Abstract By examining and comparing the linguistic patterns in a self-built corpus of Chinese-English translations produced by WeChat Translate, the latest online machine translation app from the most popular social media platform (WeChat) in China, this study explores such questions as whether or not and to what extent simplification and normalization (hypothesized Translation Universals) exhibit themselves in these translations. The results show that, whereas simplification cannot be substantiated, the tendency of normalization to occur in the WeChat translations can be confirmed. The research finds that these results are caused by the operating mechanism of machine translation (MT) systems. Certain salient words tend to prime WeChat’s MT system to repetitively resort to typical language patterns, which leads to a significant overuse of lexical chunks. It is hoped that the present study can shed new light on the development of MT systems and encourage more corpus-based product-oriented research on MT.
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Golev, N. D. "Translative Linguistics: an Aspectualized Review of Initial Provisions. Part 1. Gnoseology of Translation." Bulletin of Kemerovo State University 24, no. 6 (December 29, 2022): 717–34. http://dx.doi.org/10.21603/2078-8975-2022-24-6-717-734.
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The article introduces translative linguistics as a special branch in the study of natural languages and describes the history of its development. Translative linguistics uses the methods of quantitative linguistics, combinatorial linguistics, associative grammar, lexicography, etc. It focuses on the same aspects of language as historical grammar, phonetics, political linguistics, etc. The ontology of translational linguistics sees the natural language and its units as its research object. Translation (reserve translation, machine translation, and reverse machine translation) acts as a research method that translational linguistics uses to describe the patterns of the translated language. The author reviews various scientific publications to describe the concepts and terms of translational linguistics. The author uses the method of linguistic logic, which is understood as incorporating a new concept in the traditional system of theoretical linguistic concepts.
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Ainan Ningrum and Utami Dewi. "The Use of Google Translate and U-Dictionary as Machine Translation for Translating Text: EFL College Student’s Preference and Perceptions." ETERNAL (English Teaching Journal) 15, no. 1 (February 19, 2024): 167–79. http://dx.doi.org/10.26877/eternal.v15i1.373.
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Several studies have investigated the effects of using Google Translate and U-Dictionary on translation products for English as a foreign language (EFL) student. However, students' preferences regarding these two machines’ translation for translating texts in EFL translation classes in Indonesia still seem relatively few and have not been much explored. This research aimed to determine which machine translation EFL college students prefer for translating their text. This study used a qualitative case study approach. In investigating students’ preferences, questionnaires, and in-depth interview were used as data collection tools. Seventeen students were selected by purposive sampling and were administered an online questionnaire. Then, students participated in in-depth interviews and obtained further information from an online questionnaire. We then analyzed the data using two techniques, including frequency counts for quantitative data and qualitative analysis through thematic analysis for qualitative data. The findings reveal that students prefer Google Translate over U-Dictionary for three reasons. First, they consider Google Translate as a complete machine translation with various features that help them translate text. Second, Google Translate increases student's knowledge of new vocabulary. Third, Google Translate makes the translation process more accessible for students. It is so easy to access. However, U-Dictionary also received high marks because students felt more confident in the accuracy of the U-Dictionary translation results. Therefore, it is believed that combining these two machines’ translations will make the student's translation process more accessible
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Ding, Yiwei, and Yu Sun. "On the Replacement of Human Translation by Machine Translation in News Translation." International Journal of Languages, Literature and Linguistics 10, no. 2 (2024): 247–51. http://dx.doi.org/10.18178/ijlll.2024.10.2.520.
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After decades of development, the level of machine translation is changing day by day, and is widely used in many aspects. News translation focuses on accuracy and effectiveness at the same time. In the current information explosion era, professional news translators have to deal with a large number of scripts every day, and the use of machine translation of news scripts ensures a high accuracy rate while pursuing high efficiency. By comparing, analyzing and scoring the results of machine translation and human translation on the standard of accuracy, loyalty, and fluency, the author concludes that: machine translation is still subject to various types of errors in news translation and cannot completely replace human translation, but translators can modify the translation according to the results of machine translation, thus improving work efficiency.
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Baihaqi, Akhmad. "THE TRANSLATION RESULTS OF CHILDREN BILINGUAL STORY BOOK BETWEEN HUMAN AND MACHINE TRANSLATION: A COMPARATIVE MODEL." Cakrawala Pedagogik 5, no. 2 (October 1, 2021): 149–59. http://dx.doi.org/10.51499/cp.v5i2.260.
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The purpose of this current study is to analyze the differences of translation results of children story books between human and machine translation, especially in terms of accuracy, readability, and understandability. The method used in this work was qualitative content analysis. The children story book entitled Cindelaras served as a source of data. The original book was written in Indonesian Language, and it was published in 2001 by Gramedia Widiasarana Indonesia. The result shows that both human and machine translations deliver different lexical, grammatical, semantic, and stylistic versions in their translation results. These differences occur since the machine translation has not been able to well-recognize the context of situation and culture. This is a weakness and limitation of machine translation. Such machines cannot replace human translation. Nevertheless, the machine can serve as a pre-translation to help human translation work faster and better in producing more accurate, readable, and understandable versions.
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Shmavonyan, Gayane. "Limitations of Machine Translation." Armenian Folia Anglistika 6, no. 1-2 (7) (October 15, 2010): 130–34. http://dx.doi.org/10.46991/afa/2010.6.1-2.130.
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Along with technological advancements worldwide there is a growing demand in machine translation as well. With its various and most diverse translation opportunities, computer translation, however, has its disadvantages and restrictions. At the current stage of technological development, translation done by a translator will provide a higher quality and effectiveness if it is combined with the employment of machine translation tools.
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Oshanova, E. S., and V. N. Dekterev. "TO THE QUESTION OF ANALYSIS OF MACHINE TRANSLATION TECHNOLOGIES." Social’no-ekonomiceskoe upravlenie: teoria i praktika 18, no. 4 (December 30, 2022): 81–91. http://dx.doi.org/10.22213/2618-9763-2022-4-81-91.
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The article deals with the issue of the efficiency of translation by means of machine translation systems. The relevance of the work is related to the importance of a comprehensive analysis of machine translation systems and a detailed description of popular and well-known online translators. Scientific work in the field of machine translation is an important and urgent task, aimed at achieving equivalent translations and contributing to the solution of many applied problems associated with the development and formation of a new communication environment. In this paper, we will consider such concepts as “machine translation”, “translation equivalence”, and study machine translation systems. This article presents translation services such as the domestic service PROMT.One, the American Google Translate, as well as the German service DeepL. These services are considered leading translators with extensive features and options, and they are indispensable tools for language learning and working with foreign languages today. Thanks to machine translation systems, translations become more affordable and easier, since machine translation performs word-for-word translations that require only post-editing. However, incorrectly translated word-for-word translations can be misleading for specialists, especially inexperienced ones. It should be noted that the machine translation systems available perform translations of different quality, as a consequence, evaluation of the quality of the text translation of any orientation allows to identify the most appropriate servicefor use in any field of application.
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T S, Meenal, and Govindarajan P. "The Challenges of Using Machine Translation While Translating Polysemous Words." Studies in Media and Communication 11, no. 2 (February 22, 2023): 63. http://dx.doi.org/10.11114/smc.v11i2.5944.
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The disparities between machine translation and human translation have shown to be quite challenging when translating polysemous words. This study examines the challenges of translating polysemous words between French and English by finding an easy way to translate the polysemous terms which is the key objective. It would also help in dealing with the most effective method of translating polysemous terms, while a few succeed in translating the contexts and others struggle to come up with the right interpretations. As many words or phrases have numerous meanings, a machine translator is still unable to handle the ambiguity issues that arise and determine what the given context means. One issue that machine translators have yet to solve is the phenomena of polysemy, or numerous meanings, though it certainly isn't the only one. In this paper, instances of these machine translations of texts from several text genres—including texts from the journalistic, medical, legal, and dictionary definition genres—are examined. To clear up any misunderstanding, one must consider the situation’s background. One must also acknowledge that the situation makes sense. In the concluding section of this study, we will be shown some examples of significant issues that have arisen as a result of a lack of understanding of the term, including misunderstanding, lack of comprehension, ambiguity, and limitations on the heedless use of technical definitions in favour of common ones.
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Putra, I. Putu Ambara. "The Translation Process of Machine Translation for Cultural Terms on Balinese Folktales." Linguistika: Buletin Ilmiah Program Magister Linguistik Universitas Udayana 29, no. 1 (March 8, 2022): 27. http://dx.doi.org/10.24843/ling.2022.v29.i01.p04.
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The goal of this study is to determine the capability of Google Translate in term of translating cultural terms. This study is conducted to analyze the translation performed by one of the well-known machine translation, Google Translate. The data is collected from the translation of seven traditional Balinese folktales chosen in random via online, namely Manik Angkeran, Kebo Iwa, Lubdaka, Tampaksiring, Origin of Bali, Origin of Singaraja, and Pan Balang Tamak. The data of the study is the translation on cultural terms translated by Google Translate. The cultural terms are classified with Cultural Term Classification Theory by Newmark. The analysis is conducted by comparing the translatied text and the original text to identify the translation method utilized by Google Translate in translating cultural term into English. The Translation Method Theory and Classification by Newmark is used to identify the method of translation utilized by Google Translate. The methods then is used in order to determine the tendency of Google Translate in the translation toward cultural terms.
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Ardianto, Ardik. "TRANSLATING THAT: AN IDEATIONAL CORRESPONDENCE ANALYSIS OF MACHINE TRANSLATION." Jurnal Ilmu Sosial dan Humaniora 10, no. 1 (April 10, 2021): 11. http://dx.doi.org/10.23887/jish-undiksha.v10i1.23140.
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This paper aims to contrast the translation of two machine translation systems, Google Translate and Bing Translator, in translating the lexeme in news articles. The approach used in scrutinizing the lexeme's translation correspondence in this study is systemic functional linguistics, especially in both experiential and logical structures. This study was carried out through descriptive comparative analysis. This study's data were 40 constituents that were taken from six BBC World news articles randomly selected. A thorough analysis demonstrates that the two machine translation systems can recognize the three functions of that, i.e., Head, post-modifier, and conjunction. The highest emerging function is post-modifier by 19 times (47.5%), followed by the conjunction function by 17 times (42.5%) on the first machine translation system and 18 times (45%) on the second one. The lowest emerging function is Head by four times (10%) on the first machine translation system and three times (7.5%) on the second one. Furthermore, due to the elliptical variation of that as a relative pronoun and the translation variation of that as a post-determiner, it concludes that the translation outputs of Google Translate are more accurate, semantically acceptable, creative, and contextual than those of Bing Translator.