Relevant bibliographies by topics / Translation error

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Translation error'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Translation error"

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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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Sitepu, Julyanta Br, Abdulloh Abdulloh, and Sarsono Sarsono. "AN ERROR ANALYSIS ON STUDENTS’ TRANSLATION." Journal of English Language and Literature (JELL) 6, no. 2 (September 4, 2021): 21–30. http://dx.doi.org/10.37110/jell.v6i2.132.

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The research is conducted to find out what types of errors the students do most in translating English into Indonesian and Indonesian into English in order to improve the quality of the teaching for Private University Students. The description of the data is made by the writer based on the errors the students made in translation. The errors are articles, plural words, pronoun, noun phrases, gerund, participles, tenses, and word choices. The methodology used in this research is a qualitative research using the students’ translation from English into Indonesian and Indonesian into English. The research found the most error made by the students in translation is word choices 33.18 % or 154 errors in translating English into Indonesian and tenses 48.73% or 135 errors in translating Indonesian into English. The least error found in the students’ translation was gerund 0.64% or 3 errors in English into Indonesian and 0.72% or 2 errors Indonesian into English
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Elmgrab, Ramadan Ahmed. "Authenticity and Imitation in Translating Exposition: A Corpus-Based Study." Journal of Educational Issues 1, no. 1 (June 30, 2015): 191. http://dx.doi.org/10.5296/jei.v1i1.7781.

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<p>Many Western scholars such as Dryden show little interest in imitations, and express their preference for translations, i.e. paraphrases that are faithful to the sense of the source text. However, they consider imitations as a viable category of translation. It is the degree of freedom, or departure from the original, that differentiates a translation from an imitation. This paper is concerned with issues that are central to the understanding of English-Arabic translation errors when rendering expository text. Not surprisingly, when translating exposition, errors recur especially those relating to the linguistic competence of the students. But not all errors were the same neither was their distribution. Each text-type shows different idiosyncrasies and error distributions which indicate that performance in translation depends largely on the type of text and the rhetorical purposes as well as patterns which follow from the source text. To this end, an error corpus of linguistic structure was collected from the translation project of students majoring in translation. Syntactic, semantic, pragmatic and discoursal criteria were used to judge imitation and authenticity strategies adopted by the students during the translation process. Implications for increasing students’ awareness of the pragmatic and syntactic constraints in translating structures will also be provided.<strong> </strong></p>
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Williyan, Aldha, Umar Umar, Vica Via Vanesa, and Ai Nurul Aini. "Analyzing Translation Errors in Indonesian-English Business Letters." JELL (Journal of English Language and Literature) STIBA-IEC Jakarta 8, no. 02 (August 31, 2023): 197–208. http://dx.doi.org/10.37110/jell.v8i02.180.

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This paper discussed the translation error from a business letter made based on American Translation Association (ATA's) made by EFL Learners'. In this research, the researchers used a descriptive qualitative method. The data source in this research is two EFL Learners' who are in translation class with better English skills than other data. Data collection in this study was carried out by taking two data from a total of 36 business letter translating task data given by translation class lecturers. The data is classified based on the type of error according to ATA's Framework for Standard Error Making Classification. The results of the analysis show that there are 18 translation errors in 11 types of 26 types of translation errors with style error as the most frequently found error. Style error is the error that has the highest nominal by appearing five times out of 18 the number of errors with a percentage 27,8%. Finding errors from the two data proves that EFL Learners' who are studying translation studies still have deficiencies. Educators and students must work together so that translation errors produced by EFL Learners' can be reduced.
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Arono, Arono, and Nadrah Nadrah. "STUDENTS’ DIFFICULTIES IN TRANSLATING ENGLISH TEXT." JOALL (Journal of Applied Linguistics & Literature) 4, no. 1 (February 22, 2019): 88–99. http://dx.doi.org/10.33369/joall.v4i1.7384.

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Nowdays, there are many translation problems although software application to assist translation are available. The objectives of this research were to identify types of error in translation, students’ difficulties in translating text, and factors which influence students’ error in translating in English department of State Institute for Islamic Studies Bengkulu. This research used descriptive quantitative method. The results of this research showed that students’ difficulties in translating English text, were elliptical errors (67.29%), idioms (87.5%), and textual meaning (73.54). The difficulties of students in translating were lack of vocabulary (87,50%), difficult translating Islamic texts (75,00%), literary works (66,66%), and grammatical issues (62,50%). Then, the factors affected students’ error in translation were ignorance of ellipsis; unable to identify ellipsis, idiom, and lexical meaning; lack of strategy in translating ellipsis, idiom, and lexical meaning; translating words per word; most students lack a strong background on the content of the text. It was concluded that the students got three types of error in transalation, four points difficulties in translation, and six factors which influence the students’ error in translation.Keywords: Translation, difficulties, and English text
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Ilani, Ali, and Hossein Barati. "Translations of Journalistic Texts in Iranian Undergraduate Students: An Error Analysis Approach." International Journal of English Linguistics 6, no. 6 (November 24, 2016): 147. http://dx.doi.org/10.5539/ijel.v6n6p147.

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<p>Translating journalistic text has been one of the major courses in Iranian universities. The challenges hidden in translating journalistic texts motivated the present study to investigate the translation of such texts. Thus, this research makes an attempt to identify and categorize the probable errors and to distinguish the most frequent ones. Furthermore, it tries to find whether there is a pattern among the errors committed by students in their translations. To this end, a translation test of Persian journalistic texts was developed. Forty students studying English translation were recruited for this study. In order to analyze collected data, Keshavarz’s Model (1997) and ATA were used for error analysis. The current study found that there is not a pattern among errors committed by students. The most frequent errors were categorized as (i) grammar, (ii) terminology, and (iii) misunderstanding of original text.</p>
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Utami, Ni Putu Laksmi Dewi, and Ni Made Verayanti Utami. "Unveiling Semantic Errors Found in Lexical Translations of Tasya Farasya’s Tiktok Account." Lingua Cultura 17, no. 2 (November 13, 2023): 219–25. http://dx.doi.org/10.21512/lc.v17i2.10435.

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The research observed the semantic errors in lexis that occurred in the translation by TikTok machine translation. It became the main issue in translation studies because the accuracy of translation produced by machine translation was still questionable and debatable. The research aimed to identify the types of semantic error in lexis made by TikTok auto machine translation found in Tasya Farasya TikTok’s account and suggested a more appropriate translation. The research applied a descriptive qualitative method to analyze the error in translation produced by TikTok machine translation. The theory proposed by Sayogie (2014) was used to classify the data based on semantic aspects: grammatical meaning, contextual meaning, and referential meaning. The research results show that three types of errors are found, and the most frequent error found is an error in contextual meaning. TikTok machine translation is incapable of translating accurately because it does not know the context of the situation and translates it literally. Based on research findings, TikTok users cannot entirely rely on machine translation because it still has weaknesses in translating several terms. Thus, it is highly important that TikTok should evaluate and improve the quality of the machine translation.
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Prasetio, Noor, and Neneng Sri Wahyuningsih. "An Analysis of the Error Translation in Movie Trailers by Youtube Auto-Translate." Eligible : Journal of Social Sciences 2, no. 2 (November 27, 2023): 264–78. http://dx.doi.org/10.53276/eligible.v2i2.81.

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In this study, the writer discusses common errors done by YouTube Auto-Translate in translating some of movie trailers with Indonesian subtitle as the Target Language (TL). Then, the writer compares the subtitles in the target language to that of a professional translation’s output and YouTube's Auto-Translate output and categorizes the errors by referring to the error classification by Vilar et al. The writer found 14 errors on YouTube Auto-Translate output. After observing the 14 data, it showed that commonly errors found from the data are related to lexical level by nine times (63%). The second error type is related to disambiguation which took place four times (27%). The errors found with the lowest frequency are Word Order and Unknown Word with which each of them is only shown once (5%). To sum up, machine translation helps us a lot in looking up words in a dictionary, but it is not recommended to rely on machine translation as it fails to recognize the context. It is then suggested that whenever we use MT, we must have it post-edit by human translator.
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Vardaro, Jennifer, Moritz Schaeffer, and Silvia Hansen-Schirra. "Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing." Informatics 6, no. 3 (September 17, 2019): 41. http://dx.doi.org/10.3390/informatics6030041.

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This study aims to analyse how translation experts from the German department of the European Commission’s Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator, post-editor as well as revisor. Even though we focus on neural machine-translated segments, translator and post-editor are used synonymously because of the combined workflow using CAT-Tools as well as machine translation. Only the distinction between post-editor, which refers to a DGT translation expert correcting the neural machine translation output, and revisor, which refers to a DGT translation expert correcting the post-edited version of the neural machine translation output, is important and made clear whenever relevant. Using an automatic error annotation tool and the more fine-grained manual error annotation framework to identify characteristic error categories in the DGT texts, a corpus analysis revealed that quality assurance measures by post-editors and revisors of the DGT are most often necessary for lexical errors. More specifically, the corpus analysis showed that, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same error type in the same post-edited sentence, suggesting that the DGT experts were being primed by the NMT output. Subsequently, we designed a controlled eye-tracking and key-logging experiment to compare participants’ eye movements for test sentences containing the three identified error categories (mistranslations, terminology or stylistic errors) and for control sentences without errors. We examined the three error types’ effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path durations). Linear mixed-effects regression models predict what kind of behaviour of the DGT experts is associated with the correction of different error types during the post-editing process.
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Jufriadi, Amalia Asokawati, and Magfirah Thayyib. "The Error Analysis of Google Translate and Bing Translator in Translating Indonesian Folklore." FOSTER: Journal of English Language Teaching 3, no. 2 (May 19, 2022): 69–79. http://dx.doi.org/10.24256/foster-jelt.v3i2.89.

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The objective of this research is to find out the lexical errors made by Google Translate and Bing Translator in translating Indonesian folklore "Princess Tandampalik" and "Sigarlaki and Limbat." The research applied the qualitative method. The data were analyzed using hybrid taxonomy of error analysis from Vilar, et al. The results of this research show that Google Translate made 103 errors in total which consist of 12 missing words, 19 errors in word order, 64 incorrect words, and 8 unknown words. Meanwhile, Bing Translator made 95 errors which consist of 5 missing words, 1 error in word order, 88 incorrect words, and 1 unknown word. Incorrect word is the most frequent error found in the translation resulted from Google Translate and Bing Translator with a total of 152 errors. The incorrect words mainly occurred in the translation of adjectives and adverbs in which Google Translate and Bing Translator mostly translated them into noun form. Thus, it can be concluded that both machine translators' performances are not different because they have their advantages and disadvantages.
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Dissertations / Theses on the topic "Translation error"

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Kim, Ahrii. "Neural machine translation evaluation & error analysis in a Spanish-Korean translation." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667853.

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From RBMT to SMT and NMT, the MT field witnessed, first, a conceptual turn —from rule-based to data-base— and now, a technological turn —from MT algorithm to ML algorithm. Now that NMT became a new state of the art, this thesis quested for evaluating its performance in a Spanish-to-Korean translation, which, for the best of our knowledge, was the first attempt in this regard. The results reported that the NMT-based Google Translate (GNMT) had about 78% of reliability. In an experiment with post-editing, the post-editing was 37% more productive in GNMT than translation from scratch. An important finding was obtained from quantitative and qualitative error analysis. It reported that only 6% of the errors detected in the dataset were a syntactic error in such a distant pair like this. The results of this thesis served as a proof of a promising future of NMT in distant pairs.
Des de la Traducció Automàtica (TA) basada en regles a la TA estadística i la TA neuronal (TAN), el camp de la TA va presenciar, primer, un gir conceptual - des d'aproximacions basades en regles fins aproximacions basades en dades- i ara, un gir tecnològic –de l’algoritme de la TA al d'Aprenentatge Automàtic. Ara que la TAN s'ha convertit en un nou estat de l'art, busquem avaluar el seu grau de qualitat en la traducció de l'espanyol al coreà,. Aquest estudie constitueix, segons el nostre coneixement, el primer que intenta avaluar aquest parell de llengües. Els resultats informen que Google Translate, basada en la TAN té al voltant el 78% de fiabilitat. En un experiment amb postedició, la postedició és un 37% més productiva que la traducció des de zero. Apartir d'una anàlisi d'errors quantitativa i qualitativa hem pogut fer constatar que només el 6% dels errors detectats van ser de naturalesa sintàctica en un parell de llengües tan distant com aquest. Els resultats obtinguts en aquesta tesi van servir com a prova per a un futur prometedor de la TAN en parells distants.
Desde la Traducción Automática (TA) basada en reglas a la TA estadística y la TA neuronal (TAN), el campo de la TA presenció, primero, un giro conceptual —desde aproximaciones basadas en reglas hasta aproximaciones basadas en datos— y ahora, un giro tecnológico —del algoritmo de la TA al de Aprendizaje Automático. Ahora que la TAN se ha convertido en un nuevo estado del arte, buscamos evaluar su desempeño en la traducción del español al coreano, que constituye, según nuestro conocimiento, el primer intento al respecto. Los resultados informan que Google Translate basada en la TAN tenía alrededor del 78% de fiabilidad. En un experimento con posedición, la posedición es un 37% más productiva que la traducción desde cero. Obtuvimos un hallazgo importante a partir de un análisis de errores cuantitativo y cualitativo. Informamos que solo el 6% de los errores detectados fueron sintácticos en un par de lenguas tan distante como este. Nuestros resultados sirvieron como prueba para un futuro prometedor de la TAN en pares distantes.
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Na, Pham Phu Quynh. "Error analysis in Vietnamese - English translation : pedagogical implications." Thesis, View thesis, 2005. http://handle.uws.edu.au:8081/1959.7/20242.

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The aim of this study is to investigate the extent to which the typological differences between Vietnamese and English influence the process of translating authentic Vietnamese sentences into English through an error analysis of the Vietnamese-English translations by Vietnamese EFL students. It starts with the assumption that Vietnamese is a topic-prominent language and the basic structure of Vietnamese manifests a topic-comment relation, rather than a subject-predicate relation (Thompson, 1987; Dyvik, 1984; Hao, 1991; Rosén, 1998), and tries to find out whether the students are more likely to make more errors when the topic of the sentence is not identical with the grammatical subject. This study also investigates the most common types of errors Vietnamese students make when translating topic-comment structures from Vietnamese into English. The analysis focuses on the errors made when translating the dropped subject and empty elements of Vietnamese. This is important, given the fact that the grammatical subject is always required in English, but not in Vietnamese. The data was collected from 95 students of English translation classes in their first, second, third, and fourth years in the Department of English Language and Literature at the University of Social Sciences and Humanities, Ho Chi Minh City, Vietnam. Using an error analysis technique often adopted in studying the deviated forms produced by second language learners (James, 1998; Richards, 1974; Corder, 1974), the study constructs an error corpus in the form of a Microsoft Excel Spreadsheet and classifies all the errors based on the categories they belong to (linguistic, comprehension or translational) and the kind of deviation they are (addition, omission, misordering or misselection, etc). The study establishes a taxonomy of errors, which includes three main categories: linguistic errors, comprehension errors and translation errors. The results of the study suggest a number of potential errors students are prone to making when translating the topic-comment structure of Vietnamese into English, and provides some practical guidelines for teachers, so that they can help students deal with these types of errors in Vietnamese-English translations.
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Na, Pham Phu Quynh. "Error analysis in Vietnamese - English translation pedagogical implications /." View thesis, 2005. http://handle.uws.edu.au:8081/1959.7/20242.

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Thesis (Ph.D.) -- University of Western Sydney, 2005.
"A thesis submitted to the School of Humanities and Languages of the University of Western Sydney, College of Arts, School of Humanities and Languages, in fulfillment for the requirements of the degree of Doctor of Philosophy, December 2005." Includes bibliographical references and appendices.
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Dürlich, Luise. "Automatic Recognition and Classification of Translation Errors in Human Translation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420289.

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Grading assignments is a time-consuming part of teaching translation. Automatic tools that facilitate this task would allow teachers of professional translation to focus more on other aspects of their job. Within Natural Language Processing, error recognitionhas not been studied for human translation in particular. This thesis is a first attempt at both error recognition and classification with both mono- and bilingual models. BERT– a pre-trained monolingual language model – and NuQE – a model adapted from the field of Quality Estimation for Machine Translation – are trained on a relatively small hand annotated corpus of student translations. Due to the nature of the task, errors are quite rare in relation to correctly translated tokens in the corpus. To account for this,we train the models with both under- and oversampled data. While both models detect errors with moderate success, the NuQE model adapts very poorly to the classification setting. Overall, scores are quite low, which can be attributed to class imbalance and the small amount of training data, as well as some general concerns about the corpus annotations. However, we show that powerful monolingual language models can detect formal, lexical and translational errors with some success and that, depending on the model, simple under- and oversampling approaches can already help a great deal to avoid pure majority class prediction.
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Altice, Nathan. "I Am Error." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/405.

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I Am Error is a platform study of the Nintendo Family Computer (or Famicom), a videogame console first released in Japan in July 1983 and later exported to the rest of the world as the Nintendo Entertainment System (or NES). The book investigates the underlying computational architecture of the console and its effects on the creative works (e.g. videogames) produced for the platform. I Am Error advances the concept of platform as a shifting configuration of hardware and software that extends even beyond its ‘native’ material construction. The book provides a deep technical understanding of how the platform was programmed and engineered, from code to silicon, including the design decisions that shaped both the expressive capabilities of the machine and the perception of videogames in general. The book also considers the platform beyond the console proper, including cartridges, controllers, peripherals, packaging, marketing, licensing, and play environments. Likewise, it analyzes the NES’s extension and afterlife in emulation and hacking, birthing new genres of creative expression such as ROM hacks and tool-assisted speed runs. I Am Error considers videogames and their platforms to be important objects of cultural expression, alongside cinema, dance, painting, theater and other media. It joins the discussion taking place in similar burgeoning disciplines—code studies, game studies, computational theory—that engage digital media with critical rigor and descriptive depth. But platform studies is not simply a technical discussion—it also keeps a keen eye on the cultural, social, and economic forces that influence videogames. No platform exists in a vacuum: circuits, code, and console alike are shaped by the currents of history, politics, economics, and culture—just as those currents are shaped in kind.
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Elliott, Debra. "Corpus-based machine translation evaluation via automated error detection in output texts." Thesis, University of Leeds, 2006. http://etheses.whiterose.ac.uk/221/.

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Since the emergence of the first fully automatic machine translation (MT) systems over fifty years ago, the use of MT has increased dramatically. Consequently, the evaluation of MT systems is crucial for all stakeholders. However, the human evaluation of MT output is expensive and time-consuming, often relying on subjective quality judgements and requiring human `reference translations' against which the output is compared. As a result, interest in more recent years has turned towards automated evaluation methods, which aim to produce scores that reflect human quality judgements. As the majority of published automated evaluation methods still require human `reference translations' for comparison, the goal of this research is to investigate the potential of a method that requires access only to the translation. Based on detailed corpus analyses, the primary aim is to devise methods for the automated detection of particular error types in French-English MT output from competing systems and to explore correlations between automated error counts and human judgements of a translation as a whole. First, a French-English corpus designed specifically for MT evaluation was compiled. A sample of MT output from the corpus was then evaluated by humans to provide judgements against which automated scores would ultimately be compared. A datadriven fluency error classification scheme was subsequently developed to enable the consistent manual annotation of errors found in the English MT output, without access to the original French text. These annotations were then used to guide the selection of error categories for automated error detection, and to facilitate the analysis of particular error types in context so that appropriate methods could be devised. Manual annotations were further used to evaluate the accuracy of each automated approach. Finally, error detection algorithms were tested on English MT output from German, Italian and Spanish to determine the extent to which methods would need to be adapted for use with other language pairs.
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Zhang, Jingji. "Accuracy of mRNA Translation in Bacterial Protein Synthesis." Doctoral thesis, Uppsala universitet, Struktur- och molekylärbiologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262901.

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Reading of messenger RNA (mRNA) by aminoacyl-tRNAs (aa-tRNAs) on the ribosomes in the bacterial cell occurs with high accuracy. It follows from the physical chemistry of enzymatic reactions that there must be a trade-off between rate and accuracy of initial tRNA selection in protein synthesis: when the current accuracy, the A-value, approaches its maximal possible value, the d-value, the kinetic efficiency of the reaction approaches zero. We have used an in vitro system for mRNA translation with purified E. coli components to estimate the d- and A-values by which aa-tRNAs discriminate between their cognate and near cognate codons displayed in the ribosomal A site. In the case of tRNALys, we verified the prediction of a linear trade-off between kinetic efficiency of cognate codon reading and the accuracy of codon selection. These experiments have been extended to a larger set of tRNAs, including tRNAPhe, tRNAGlu, tRNAHis, tRNACys, tRNAAsp and tRNATyr, and linear efficiency-accuracy trade-off was observed in all cases. Similar to tRNALys, tRNAPhe discriminated with higher accuracy against a particular mismatch in the second than in the first codon position. Remarkably high d-values were observed for tRNAGlu discrimination against a C-C mismatch in the first codon position (70 000) and for tRNAPhe discrimination against an A-G mismatch in the second codon position (79 000). At the same time, we have found a remarkably small d-value (200) for tRNAGlu misreading G in the middle position of the codon (U-G mismatch). Aminoglycoside antibiotics induce large codon reading errors by tRNAs. We have studied the mechanism of aminoglycoside action and found that the drug stabilized aminoacyl-tRNA in a codon selective in relation to a codon non-selective state. This greatly enhanced the probability of near cognate aminoacyl-tRNAs to successfully transcend the initial selection step of the translating ribosome. We showed that Mg2+ ions, in contrast, favour codon non-selective states and thus induce errors in a principally different way than aminoglycosides.  We also designed experiments to estimate the overall accuracy of peptide bond formation with, including initial selection accuracy and proofreading of tRNAs after GTP hydrolysis on EF-Tu. Our experiments have now made it possible to calibrate the accuracy of tRNA selection in the test tube to that in the living cells. We will now also be able to investigate the degree to which the accuracy of tRNA selection has been optimized for maximal fitness.
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Megrad, Ramadan Ahmed. "Error assessment in the teaching of translation : a case of Garyounis University, Libya." Thesis, University of Leeds, 1999. http://etheses.whiterose.ac.uk/2799/.

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The research investigates the ways in which the needs of a particular translation teaching-situation are provided for. The argument runs against the general practice where a translation model is independently adopted and is thought to provide the teacher with the necessary methodological and pedagogical background. The study demonstrates that active interaction rather than the passive reception from the teacher within the existing models is essential. This is possible in the light of a product-based analysis of actual training in which the identification of a translation problem must precede the development or adoption of a theory of translation. Error analysis offers in this case the appropriate tool to check the students' needs in a particular training situation in terms of the actual text being translated. In the event of an error analysis, three main interdependent processes should be observed: diagnosis of the deficiency, evaluation of its gravity and recommendation of the appropriate translation teaching therapy. On the basis of an analysis of Arabic/English trainees' performance and teachers' evaluation, we have identified a number of problems relating to the students' use of language and translation skills, and teachers' assessment of their trainees' errors. A two-stage translation course is recommended accordingly. The frrst is preparatory; it serves to eliminate the students' language deficiencies and provide the necessary background for teachers to devise the appropriate translation teaching tools. The second emphasises their needs in terms of translation skills, which our results show, are best identified and represented in a text-typological format.
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Navangul, Gaurav D. "Stereolithography (STL) File Modification by Vertex Translation Algorithm (VTA) for Precision Layered Manufacturing." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1306500244.

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Marcassoli, Giulia. "Gli output dei sistemi di traduzione automatica neurale: valutazione della qualità di Google Translate e DeepL Translator nella combinazione tedesco-italiano." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19536/.

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MT is becoming a powerful tool for professional translators, language service providers and common users. The present work focuses on its quality, evaluating the translations produced by two neural MT systems – i.e. Google Translate and DeepL Translator – through manual error annotation. The data set used for this task is composed of semi-specialized German texts translated into Italian. Aim of the present work is to assess the quality of MT outputs for the data set considered and obtain a detailed overview of the type of errors made by the two neural MT systems examined. The first part of this work provides a theoretical background for MT and its evaluation. Chapter 1 deals with the definition of MT and summarizes its history. Moreover, a detailed analysis of the different MT architectures is provided, as well as an overview of the possible application scenarios and the different categories of users. Chapter 2 introduces the notion of quality in the translation field and the main automatic and manual methods applied to MT quality assessment tasks. A comprehensive analysis of some of the most significant studies on neural and phrase-based MT systems output quality is then provided. The second part of this work presents a quality assessment of the output produced by two neural MT systems, i.e. Google Translation and DeepL Translator. The evaluation was performed through manual error annotation based on a fine-grained error taxonomy. Chapter 3 outlines the methodology followed during the evaluation, with a description of the dataset, the neural MT systems chosen for the study, the annotation tool and the taxonomy used during the annotation task. Chapter 4 provides the results of the evaluation and a comment thereof, offering examples extracted from the annotated data set. The final part of this work summarizes the major findings of the present contribution. Results are then discussed, with a focus on their implication for future work.
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Books on the topic "Translation error"

1

Yi ben bi jiao yu zheng wu: Version comparison & error detection in translation. Beijing Shi: Beijing da xue chu ban she, 2011.

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Ghazzālī. Al-Ghazālī's Path to Sufism and his Deliverance from error: An annotated translation of al-Munqidh min al-dal⁻al. Louisville, KY: Fons Vitae, 2006.

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Ying yu zhuan ye ba ji gai cuo yu fan yi 100+100: Error correction and translation of TEM-8. Dalian Shi: Dalian li gong da xue chu ban she, 2003.

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Joseph, McCarthy Richard, ed. Al-Ghazālī's Path to Sufism and his Deliverance from error: An annotated translation of al-Munqidh min al Dalp-sal. Louisville, KY: Fons Vitae, 2000.

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Ghazzālī. Deliverance from error: An annotated translation of al-Munqidh min al Dal⁻al and other relevant works of Al-Ghaz⁻al⁻i. Louisville, KY: Fons Vitae, 1999.

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Ghazzālī. Deliverance from error: An annotated translation of al-Munqidh min al Dal⁻al and other relevant works of Al-Ghaz⁻al⁻i. Louisville, KY: Fons Vitae, 1999.

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Sabourin, Conrad. Computer assisted language teaching: Teaching vocabulary, grammar, spelling, writing, composition, listening, speaking, translation, foreign languages, text composition aids, error detection and correction, readability analysis : bibliography. Montréal: Infolingua, 1994.

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Ovid. Culpa silenda: Le elegie dell'"error" ovidiano. Bari: Edipuglia, 2002.

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illustrator, Pinder Andrew, ed. Utterly lost in translation: Even more misadventures in English abroad. London: Metro Publishing, 2015.

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Oyŏk ŭi munhwa: The culture of mistranslation. Sŏul-si: Somyŏng Ch'ulp'an, 2014.

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Book chapters on the topic "Translation error"

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Fishel, Mark, Ondřej Bojar, Daniel Zeman, and Jan Berka. "Automatic Translation Error Analysis." In Text, Speech and Dialogue, 72–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23538-2_10.

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Malmkjaer, Kirsten. "Censorship or error." In Claims, Changes and Challenges in Translation Studies, 141–55. Amsterdam: John Benjamins Publishing Company, 2004. http://dx.doi.org/10.1075/btl.50.12mal.

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Altman, Janet. "Error analysis in the teaching of simultaneous interpretation." In Benjamins Translation Library, 25. Amsterdam: John Benjamins Publishing Company, 1994. http://dx.doi.org/10.1075/btl.3.05alt.

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Alenazi, Yasir. "Semantic Lexical Error Analysis." In Exploring Lexical Inaccuracy in Arabic-English Translation, 99–130. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6390-2_5.

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Alenazi, Yasir. "Formal Lexical Error Analysis." In Exploring Lexical Inaccuracy in Arabic-English Translation, 75–97. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6390-2_4.

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Almahasees, Zakaryia. "Error analysis for English-Arabic translation." In Analysing English-Arabic Machine Translation, 77–130. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003191018-4.

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Xiong, Deyi, and Min Zhang. "Translation Error Detection with Linguistic Features." In Linguistically Motivated Statistical Machine Translation, 125–35. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-356-9_8.

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Liu, Mengyi, Jian Tang, Yu Hong, and Jianmin Yao. "Terminology Translation Error Identification and Correction." In Communications in Computer and Information Science, 141–52. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6805-8_12.

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Pym, Anthony. "Translation error analysis and the interface with language teaching." In Teaching Translation and Interpreting, 279. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/z.56.42pym.

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Du, Jinhua, Junbo Guo, Sha Wang, and Xiyuan Zhang. "Multi-classifier Combination for Translation Error Detection." In Lecture Notes in Computer Science, 291–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41491-6_27.

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Conference papers on the topic "Translation error"

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Song, Kaitao, Xu Tan, and Jianfeng Lu. "Neural Machine Translation with Error Correction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/538.

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Neural machine translation (NMT) generates the next target token given as input the previous ground truth target tokens during training while the previous generated target tokens during inference, which causes discrepancy between training and inference as well as error propagation, and affects the translation accuracy. In this paper, we introduce an error correction mechanism into NMT, which corrects the error information in the previous generated tokens to better predict the next token. Specifically, we introduce two-stream self-attention from XLNet into NMT decoder, where the query stream is used to predict the next token, and meanwhile the content stream is used to correct the error information from the previous predicted tokens. We leverage scheduled sampling to simulate the prediction errors during training. Experiments on three IWSLT translation datasets and two WMT translation datasets demonstrate that our method achieves improvements over Transformer baseline and scheduled sampling. Further experimental analyses also verify the effectiveness of our proposed error correction mechanism to improve the translation quality.
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Subramanian, Krishna, Dave Stallard, Rohit Prasad, Shirin Saleem, and Prem Natarajan. "Semantic translation error rate for evaluating translation systems." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430144.

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Chandra, Julius. "Translation Errors on Public Place Signboards: An Error Analysis and Translation Strategies Applied." In Seminar Nasional Struktural 2018. Semarang, Indonesia: Dian Nuswantoro University, 2018. http://dx.doi.org/10.33810/274177.

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Dumitran, Angela. "TRANSLATION ERROR IN GOOGLE TRANSLATE FROM ENGLISH INTO ROMANIAN IN TEXTS RELATED TO CORONAVIRUS." In eLSE 2021. ADL Romania, 2021. http://dx.doi.org/10.12753/2066-026x-21-078.

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Both the emergence of the pandemic and lack of knowledge and/or time needed to translate texts related to this topic brought about an increased interest in analyzing Neural Machine Translation (NMT) performance. This study aims to identify and analyze lexical and semantic errors of language aspects that appear in medical texts translated by Google Translate from English into Romanian. The data used for investigation comprises official prospects of 5 vaccines that were approved to be used against the current coronavirus. The focus is on the lexical and semantic errors, as researchers state that these errors made by Machine Translation have the highest frequency compared to morphological or syntactic errors. Moreover, the lexical errors may affect the meaning, the message, and may easily lead to mistranslation, misunderstanding and, therefore, misinformation. The texts to be analyzed are collected from official websites and translated using Google Translate and Google Languages Tools. From the data analyzed, there are 22 lexical and semantic errors that are approached through descriptive methodology. By examining types of errors in translation from English into Romanian and analyzing the potential causes of errors, the results will be used to illustrate the quality and accuracy of Google Translate when translating public health information from English into Romanian, to observe how much the message is affected by the error, in order to sharpen up linguistic awareness. The results of the study can ultimately help improve of the quality of NMT in terms of better lexical selection and attempt to give inputs as a contribution for a more adequate translation into Romanian by Google Machine Translation.
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Stambolieva, Maria. "Corpus Linguistics, Translation and Error Analysis." In Second Workshop on Human-Informed Translation and Interpreting Technology. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/issn.2683-0078.2019_012.

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Ishikawa, Kai, and Eiichiro Sumita. "Error correction translation using text corpora." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-440.

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Fomicheva, Marina, Lucia Specia, and Nikolaos Aletras. "Translation Error Detection as Rationale Extraction." In Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-acl.327.

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Papadopoulou, Martha Maria, Anna Zaretskaya, and Ruslan Mitkov. "Benchmarking ASR Systems Based on Post-Editing Effort and Error Analysis." In TRanslation and Interpreting Technology ONline. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-071-7_023.

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Rozovskaya, Alla, and Dan Roth. "Grammatical Error Correction: Machine Translation and Classifiers." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1208.

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Yuan, Zheng, and Ted Briscoe. "Grammatical error correction using neural machine translation." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/n16-1042.

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Reports on the topic "Translation error"

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Kuzmina, Aleksandra, Amalia Kuregyan, and Ekaterina Pertsevaya. PSUDOINTERNATIONAL WORDS IN THE TRANSLATION OF ECONOMIC TEXTS CARRIED OUT BY THE STUDENTS OF NON-LINGUISTIC UNIVERSITIES. Crimean Federal University named after V.I. Vernadsky, 2023. http://dx.doi.org/10.12731/ttxnbz.

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The article deals with the problems of translating pseudo-international words in economic texts. Incorrect interpretations of pseudo-international words in written texts and oral translations are investigated. It is noted that errors in the written version appear mainly due to the use of the most common full-text translation services, where the word spelling is a priority. For oral translation, the first variant of incorrect interpretation is more typical, when the word is pronounced similarly to Russian, but is not its analogue. The paper presents the classification of pseudo-international words according to the parts of speech: noun, adjective, verb and adverb, and also provides typical mistakes that students make when translating this vocabulary. The authors of the article also present tasks that are the most effective way to overcome misinterpretations of words related to pseudo-internationalisms.
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Condon, Sherri, Dan Parvaz, John Aberdeen, Christy Doran, Andrew Freeman, and Marwan Awad. Evaluation of Machine Translation Errors in English and Iraqi Arabic. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada576234.

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