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Journal articles on the topic 'Automatic translation systems'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Zhao, Tao, and Mazni Binti Alias. "Automated programming approaches to enhance computer-aided translation accuracy." PeerJ Computer Science 10 (November 12, 2024): e2396. http://dx.doi.org/10.7717/peerj-cs.2396.

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With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today’s fast-paced environment. Computer-assisted translation (CAT) addresses the issue of slow translation speed; however, the quality of CAT translations still requires rigorous evaluation. This study aims to answer the following questions: How d

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Ulitkin, Ilya, Irina Filippova, Natalia Ivanova, and Alexey Poroykov. "Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment." E3S Web of Conferences 284 (2021): 08001. http://dx.doi.org/10.1051/e3sconf/202128408001.

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We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n-gram models, make it possible to compare the quality of machine translation to reference translation. We employ modern metrics for automatic evaluation of machine translation quality such as BLEU, F-measure, and TER to compare translations made by Google and PROMT neural machine translation systems with translations obtained 5 years ago, when statistical machine translation and rule-based machine translation al

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Krivoturov, Iurie. "Certain Linguistic Issues of Machine Translation." Intertext, no. 1(59) (July 2022): 68–78. http://dx.doi.org/10.54481/intertext.2022.1.08.

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The article considers the possibility of translating literary texts from a natural language into a target language using an automatic machine translation system. The author considers the possibilities of biological intelligence in comparison with machine "intelligence", which is a prototype of artificial intelligence. The paper gives an example (based on the game in GO) of the emergence of neural networks that appear as a result of self-learning of automated systems. New types of neural networks create an algorithm that always wins (the game of GO) against a person, even with the title of “wor

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Chen, Jianhong. "Analysis of Intelligent Translation Systems and Evaluation Systems for Business English." Journal of Mathematics 2022 (January 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/5952987.

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In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation featur

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Mahesh, Vanjani, and Aiken Milam. "A Comparison of Free Online Machine Language Translators." Journal of Management Science and Business Intelligence 5, no. 1 (2020): 26–31. https://doi.org/10.5281/zenodo.3961085.

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Automatic Language Translators also referred to as machine translation software automate the process of language translation without the intervention of humans While several automated language translators are available online at no cost there are large variations in their capabilities. This article reviews prior tests of some of these systems, and, provides a new and current comprehensive evaluation of the following eight: Google Translate, Bing Translator, Systran, PROMT, Babylon, WorldLingo, Yandex, and Reverso. This research could be helpful for users attempting to explore and decide which

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Tezcan, Arda, and Bram Bulté. "Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation." Information 13, no. 1 (2022): 19. http://dx.doi.org/10.3390/info13010019.

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Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable improvements in translation quality, as assessed by a limited set of automatic evaluation metrics. In this study, we extend this evaluation by calculating a wider range of automated quality metrics that tap into different aspects of translation quality and by performing manual MT error analysis. Moreover, we investigate in more detail how fuzzy

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Samanta, Swadesh Kumar, John Woods, and Mohammed Ghanbari. "Automatic Language Translation." International Journal of Technology and Human Interaction 7, no. 1 (2011): 1–18. http://dx.doi.org/10.4018/jthi.2011010101.

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In this paper, the authors demonstrate that language diversity imposes a significant barrier in message communication like Short Messaging Service (SMS). SMS and other messaging services, including Multimedia Messaging Service (MMS) and e-mail, are widely used for person-to-person and Business-to-Consumer (B2C) communications due to their reach, simplicity and reliability of delivery. Reach and service delivery can be further enhanced if the message is delivered in the recipient’s preferred language. Using language translation software and a database server, the authors show that the messages

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Mehta, Sneha, Bahareh Azarnoush, Boris Chen, et al. "Simplify-Then-Translate: Automatic Preprocessing for Black-Box Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8488–95. http://dx.doi.org/10.1609/aaai.v34i05.6369.

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Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that “simplifies” the original sentence to be more conducive for translation. The model is used to preprocess sou

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Kozhirbayev, Zhanibek, and Talgat Islamgozhayev. "Cascade Speech Translation for the Kazakh Language." Applied Sciences 13, no. 15 (2023): 8900. http://dx.doi.org/10.3390/app13158900.

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Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large col

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Wu, Diming, Mingke Wang, and Xiaomin Li. "Automatic Scoring for Translations Based on Language Models." Computational Intelligence and Neuroscience 2022 (June 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/2171206.

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With the development of English education, translation scoring has gradually become a time-consuming and energy-consuming task, and it is difficult to ensure objectivity because of the subjective factors in manual correcting. Due to the similarity between the quality evaluation of responses generated by the dialogue system and the translation results submitted by students, we selected two metrics of dialogue to automatically score the translations, which are applied in a case study. The experiments show that the hybrid scores of two metrics are close to human scores. In conclusion, the method

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Ning, Jing, and Haidong Ban. "Design and Testing of Automatic Machine Translation System Based on Chinese-English Phrase Translation." Mobile Information Systems 2021 (September 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/3539155.

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With the development of linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used. The automatic expression translation method based on the Chinese-English machine takes short sentences as the basic translation unit and makes full use of the order of short sentences. Compared with word-based statistical machine translation methods, the effect is greatly improved. The performance of machine translation is constantly improving. This article aims to study the design of phrase-based automatic machine translation s

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Beliaeva, Larisa, and Olga Kamshilova. "Lexicographic Problems of Machine Translation Systems: On the Way from Literal to Neural." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije 23, no. 5 (2024): 6–19. https://doi.org/10.15688/jvolsu2.2024.5.1.

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The article discusses some current issues of interpreting out-of-vocabulary words by modern machine translation systems (MT systems) in the context of changing forms and ways of maintaining an automatic dictionary. It provides a critical outline of the typology of MT systems and strategies for their development. It describes the impact of fast developing software and technologies on these strategies and analyzes the changes they bring into the forms of dictionary support. The research shows that the linguistic support and the structure of automatic dictionaries, whatever the MT system is, are

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Singh, Satwinder, P. Aswin, Yash Paul, Shafiya Mushtaq, and Rajesh Singh. "Systematic Approach for Speech to Text Translation and Summarization." Indian Journal Of Science And Technology 18, no. 28 (2025): 2314–26. https://doi.org/10.17485/ijst/v18i28.470.

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Objectives: To develop a fully automated framework capable of accurately transcribing and translating native language speech into textual form without human intervention. Focusing on multilingual speech recognition, the research aims to evaluate system performance using real-world speech data, with particular emphasis on translation fidelity and contextual accuracy. Methods: The proposed framework processes raw audio input, exemplified by Indian Prime Minister Narendra Modi’s monthly "Mann Ki Baat (मन की बात)" addresses, through a multi-stage pipeline. Initially, the system converts speech int

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Malik, Pooja, Y. Mrudula, and Anurag S. Baghel. "Statistical Analysis of Machine Translation Evaluation Systems for English- Hindi Language Pair." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 864–70. http://dx.doi.org/10.2174/2213275912666190716100145.

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Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where machine translation evaluation enter

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Safarov, Ildar M. "DEVELOPMENT OF AUTOMATIC SPEECH TRANSLATION SYSTEMS USING AI." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 8/5, no. 147 (2024): 105–16. http://dx.doi.org/10.36871/ek.up.p.r.2024.08.05.012.

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The article discusses the current state and prospects for the development of automatic speech translation systems using artificial intelligence (AI). The main problems, such as the complexity of processing live speech, taking into account accents and contextual dependencies, are described. Particular attention is paid to the implementation of deep neural networks and transformer architecture, which has significantly improved the quality of translation and the speed of data processing. Achievements in the field of transfer learning, which help to improve the accuracy of translation for less com

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Omar, Abdulfattah, and Yasser A. Gomaa. "The Machine Translation of Literature: Implications for Translation Pedagogy." International Journal of Emerging Technologies in Learning (iJET) 15, no. 11 (2020): 228. http://dx.doi.org/10.3991/ijet.v15i11.13275.

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The recent years have witnessed an increasing importance of machine translation systems due to the prolific production on online texts in different disciplines and furthermore, the inability of traditional translation methods in addressing translation needs all over the world. It is even argued that training on translation tools should be integrated into translation pedagogies and ultimately, courses should be provided for students and professionals. In spite of the effectiveness of translation tools and systems in providing solutions in relation to different disciplines and text genres, the u

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Petrova-Lyubenova, Viktoriya. "Development of semi-automatic multilingual terminological resources." Papers of the Institute for Bulgarian Language “Prof. Lyubomir Andreychin”, no. XXXVI (August 2023): 111–84. http://dx.doi.org/10.47810/pibl.xxxvi.23.05.

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The text suggests a methodology for semi-automatic creation of terminological resources for the Bulgarian language for computer-aided translation systems. For this purpose, the technical characteristics of computer-assisted translation systems and their components (translation memory, terminological base, machine translation) are described and analyzed. The results of a survey aimed at translators in Bulgaria and their opinion regarding this type of technology are described. The proposed methodology does not claim to be exhaustive, but takes into account the processes that translators follow i

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Absamatova, Gulhayo Bakhodirovna. "LINGUISTIC FEATURES OF SIGN LANGUAGE AUTOMATIZATION." International journal of word art 5, no. 3 (2022): 24–29. https://doi.org/10.5281/zenodo.6642151.

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The article discusses the basic distinguishing features of automatic translation focused on sign languages; general functional requirements for the semantic component of such a system are formulated; the main modern approaches to the construction of automatic translation systems for sign languages are outlined. Many sources of textual information and language communication channels remain inaccessible. The creation of an automatic sign language interpreter capable of bidirectional translation of texts will significantly expand the scope of the use of sign language, including, in particular, fe

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Ivanchenko, T. A. "Errors in machine translation from German into Russian (based on articles of German-language media and their translations)." Uchenye zapiski St. Petersburg University of Management Technologies and Economics, no. 4 (December 23, 2021): 30–41. http://dx.doi.org/10.35854/2541-8106-2021-4-30-41.

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The article is devoted to the study of errors and inaccuracies made by machine translation systems. The reasons for the appearance of errors of various types in the texts of machine translations of German-language articles of well-known mass media into Russian, made by popular translation programs, are analyzed. A classification of errors is given. The lexical-semantic and lexical-stylistic, normative-usual, grammatical, punctuation and spelling errors are highlighted. Typical “weaknesses” of machine translation from German into Russian are revealed, which should be paid attention to during po

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Garcia, Eva Martínez, Carles Creus, Cristina España-Bonet, and Lluís Màrquez. "Using Word Embeddings to Enforce Document-Level Lexical Consistency in Machine Translation." Prague Bulletin of Mathematical Linguistics 108, no. 1 (2017): 85–96. http://dx.doi.org/10.1515/pralin-2017-0011.

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Abstract We integrate new mechanisms in a document-level machine translation decoder to improve the lexical consistency of document translations. First, we develop a document-level feature designed to score the lexical consistency of a translation. This feature, which applies to words that have been translated into different forms within the document, uses word embeddings to measure the adequacy of each word translation given its context. Second, we extend the decoder with a new stochastic mechanism that, at translation time, allows to introduce changes in the translation oriented to improve i

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Lee, Seungjun, Jungseob Lee, Hyeonseok Moon, et al. "A Survey on Evaluation Metrics for Machine Translation." Mathematics 11, no. 4 (2023): 1006. http://dx.doi.org/10.3390/math11041006.

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The success of Transformer architecture has seen increased interest in machine translation (MT). The translation quality of neural network-based MT transcends that of translations derived using statistical methods. This growth in MT research has entailed the development of accurate automatic evaluation metrics that allow us to track the performance of MT. However, automatically evaluating and comparing MT systems is a challenging task. Several studies have shown that traditional metrics (e.g., BLEU, TER) show poor performance in capturing semantic similarity between MT outputs and human refere

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Yousef, Tariq, Chiara Palladino, Farnoosh Shamsian, and Maryam Foradi. "Translation Alignment with Ugarit." Information 13, no. 2 (2022): 65. http://dx.doi.org/10.3390/info13020065.

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Ugarit is a public web-based tool for manual annotation of parallel texts for generating word-level translation alignment. We aimed to develop a user-friendly interactive interface to visualize aligned texts and collect training data in the form of translation pairs to be used later, (i) for training an automatic translation alignment system for historical languages at the word/phrase level, (ii) as a gold standard to evaluate automatic alignment and machine translation systems. Ugarit is now widely used for learning new languages, especially historical languages, and as a reading environment

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Skacheva, Nina Vasil'evna. "Analysis of Idioms in Neural Machine Translation: A Data Set." Программные системы и вычислительные методы, no. 3 (March 2024): 55–63. http://dx.doi.org/10.7256/2454-0714.2024.3.71518.

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There has been a debate in various circles of the public for decades about whether a "machine can replace a person." This also applies to the field of translation. And so far, some are arguing, others are "making a dream come true." Therefore, now more and more research is aimed at improving machine translation systems (hereinafter MP). To understand the advantages and disadvantages of MP systems, it is necessary, first of all, to understand their algorithms. At the moment, the main open problem of neural machine translation (NMP) is the translation of idiomatic expressions. The meaning of suc

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Shiwen, Yu. "Automatic evaluation of output quality for Machine Translation systems." Machine Translation 8, no. 1-2 (1993): 117–26. http://dx.doi.org/10.1007/bf00981248.

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Ansary, MD Abdul Awal. "Neural Machine Translation Between English And Bangla: A Philosophical Survey Observing Architectures And Performances." IOSR Journal of Computer Engineering 26, no. 5 (2024): 17–25. http://dx.doi.org/10.9790/0661-2605011725.

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Neural Machine Translation (NMT) is a state of the art of machine translations that uses neural network models to translate text from one language to another. Unlike traditional machine translation methods, which rely on statistical models and rule-based systems, NMT leverages the power of deep learning, specifically using techniques such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or more recently, transformers. Neural Machine Translation represents a significant dive forward in the field of automatic translation, leveraging the latest advancements in deep le

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Fedorko, Yana, and Tetiana Yablonskaya. "The Varibility of Reproduction: Emotive Units in a Literary Text (On the material of Ukrainian, Russian and Chinese) Variation of reflection of English-speaking emotional units in the translation of an artistic work (in Ukrainian, Russian, Chinese)." Naukovy Visnyk of South Ukrainian National Pedagogical University named after K. D. Ushynsky: Linguistic Sciences 26, no. 27 (2019): 211–22. http://dx.doi.org/10.24195/2616-5317-2018-27-24.

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The article is focused on peculiarities of English and Chinese political discourse translation into Ukrainian. The advantages and disadvantages of machine translation are described on the basis of linguistic analysis of online Google Translate and M-Translate systems. The reasons of errors in translation are identified and the need of post-correction to improve the quality of translation is wanted. Key words: political discourse, automatic translation, online machine translation systems, machine translation quality assessment.

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Sperber, Matthias, Hendra Setiawan, Christian Gollan, Udhyakumar Nallasamy, and Matthias Paulik. "Consistent Transcription and Translation of Speech." Transactions of the Association for Computational Linguistics 8 (November 2020): 695–709. http://dx.doi.org/10.1162/tacl_a_00340.

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The conventional paradigm in speech translation starts with a speech recognition step to generate transcripts, followed by a translation step with the automatic transcripts as input. To address various shortcomings of this paradigm, recent work explores end-to-end trainable direct models that translate without transcribing. However, transcripts can be an indispensable output in practical applications, which often display transcripts alongside the translations to users. We make this common requirement explicit and explore the task of jointly transcribing and translating speech. Although high ac

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Qafmolla, Nejla. "Automatic Language Identification." European Journal of Language and Literature 7, no. 1 (2017): 140. http://dx.doi.org/10.26417/ejls.v7i1.p140-150.

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Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID – so as to generate more efficient syst

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Yuhan, Nataliia, Yuliia Herasymenko, Oleksandra Deichakivska, Anzhelika Solodka, and Yevhen Kozlov. "Translation as a linguistic act in the context of artificial intelligence: the impact of technological changes on traditional approaches." Data and Metadata 3 (July 12, 2024): 429. http://dx.doi.org/10.56294/dm2024429.

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The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities

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Cheryl Amaka Udogu and Oluchukwu Felicia Asadu. "A critical analysis of Igbo-English translations in social media posts: A study of Twitter and Facebook." World Journal of Advanced Research and Reviews 19, no. 1 (2023): 265–72. http://dx.doi.org/10.30574/wjarr.2023.19.1.1285.

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The purpose of this study is to conduct a critical analysis of posts and comments written in Igbo language on Facebook and Twitter, which are automatically translated into English upon user request. The main focus is to examine the accuracy of the translations provided by these social media platforms, such as Facebook and Twitter, for posts written in Igbo. Additionally, the study aims to understand the challenges presented by language barriers in communication, shedding light on the linguistic and cultural differences between Igbo and English. Furthermore, the research aims to highlight the d

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Cheryl, Amaka Udogu, and Felicia Asadu Oluchukwu. "A critical analysis of Igbo-English translations in social media posts: A study of Twitter and Facebook." World Journal of Advanced Research and Reviews 19, no. 1 (2023): 265–72. https://doi.org/10.5281/zenodo.10250619.

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The purpose of this study is to conduct a critical analysis of posts and comments written in Igbo language on Facebook and Twitter, which are automatically translated into English upon user request. The main focus is to examine the accuracy of the translations provided by these social media platforms, such as Facebook and Twitter, for posts written in Igbo. Additionally, the study aims to understand the challenges presented by language barriers in communication, shedding light on the linguistic and cultural differences between Igbo and English. Furthermore, the research aims to highlight the d

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Dashtaki, Parnyan Bahrami. "An Investigation into Methodology and Metrics Employed to Evaluate the (Speech-to-Speech) Way in Translation Systems." Modern Applied Science 11, no. 4 (2017): 55. http://dx.doi.org/10.5539/mas.v11n4p55.

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Speech-to-speech translation is a challenging problem, due to poor sentence planning typically associated with spontaneous speech, as well as errors caused by automatic speech recognition. Based upon a statistically trained speech translation system, in this study, we try to investigate methodologies and metrics employed to assess the (speech-to-speech) way in translation systems. The speech translation is performed incrementally based on generation of partial hypotheses from speech recognition. Speech-input translation can be properly approached as a pattern recognition problem by means of st

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RAPP, REINHARD, SERGE SHAROFF, and PIERRE ZWEIGENBAUM. "Preface." Natural Language Engineering 22, no. 4 (2016): 497–500. http://dx.doi.org/10.1017/s1351324916000103.

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After several decades of work on rule-based machine translation (MT) where linguists try to manually encode their knowledge about language, the time around 1990 brought a paradigm change towards automatic systems which try to learn how to translate by looking at large collections of high-quality sample translations as produced by professional translators. The first such attempts were called example- or analogy-based translation, and somewhat later the so-called statistical approach to MT was introduced. Both can be subsumed under the label data-driven approaches to MT. It took about 10 years u

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Shterionov, Dimitar, Félix do Carmo, Joss Moorkens, et al. "A roadmap to neural automatic post-editing: an empirical approach." Machine Translation 34, no. 2-3 (2020): 67–96. http://dx.doi.org/10.1007/s10590-020-09249-7.

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Abstract In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficu

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Benkova, Lucia, Dasa Munkova, Ľubomír Benko, and Michal Munk. "Evaluation of English–Slovak Neural and Statistical Machine Translation." Applied Sciences 11, no. 7 (2021): 2948. http://dx.doi.org/10.3390/app11072948.

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This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural m

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Goyal, Lalit, and Vishal Goyal. "Text to Sign Language Translation System." International Journal of Synthetic Emotions 7, no. 2 (2016): 62–77. http://dx.doi.org/10.4018/ijse.2016070104.

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Many machine translation systems for spoken languages are available, but the translation system between the spoken and Sign Language are limited. The translation from Text to Sign Language is different from the translation between spoken languages because the Sign Language is visual spatial language which uses hands, arms, face, and head and body postures for communication in three dimensions. The translation from text to Sign Language is complex as the grammar rules for Sign Language are not standardized. Still a number of approaches have been used for translating the Text to Sign Language in

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Zhou, Wei, Yuyang Gao, Xuanhe Zhou, and Guoliang Li. "Cracking SQL Barriers: An LLM-based Dialect Translation System." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–26. https://doi.org/10.1145/3725278.

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Automatic dialect translation reduces the complexity of database migration, which is crucial for applications interacting with multiple database systems. However, rule-based translation tools (e.g., SQLGlot, jOOQ, SQLines) are labor-intensive to develop and often (1) fail to translate certain operations, (2) produce incorrect translations due to rule deficiencies, and (3) generate translations compatible with some database versions but not the others. In this paper, we investigate the problem of automating dialect translation with large language models (LLMs). There are three main challenges.

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Popović, Maja, and Hermann Ney. "Towards Automatic Error Analysis of Machine Translation Output." Computational Linguistics 37, no. 4 (2011): 657–88. http://dx.doi.org/10.1162/coli_a_00072.

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Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to class

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Abadou, Fadila, and Saleh Khadich. "Coherence in Machine Translation Output." Traduction et Langues 18, no. 2 (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

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Сухоручкина, И. Н., та А. А. Сухоручкина. "Классификация разработанных и используемых в России программ автоматического и автоматизированного перевода научно-технических документов на основе искусственного интеллекта". Научно-техническая информация. Серия 1: Организация и методика информационной работы, № 5 (1 травня 2024): 22–30. https://doi.org/10.36535/0548-0019-2024-05-4.

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Проанализированы разработанные и используемые в России программы автоматического и автоматизированного перевода научно-технических документов и проектов на основе искусственного интеллекта, стандарты по информации, библиотечному и издательскому делу, системы технологической, конструкторской и программной документации, электронные словари и тезаурусы, базы данных памяти переводов, правила оформления переводов научно-технических документов и проектов, программы распознавания текстов, символов и редактирования переводов. Классифицированы и ранжированы по количеству языков используемые в России 22

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Te, Taka Keegan, and Cairns Jasmin. "Microsoft Translator Hub for Māori Language." International Journal of Computer Science Issues 15, no. 3 (2018): 8–16. https://doi.org/10.5281/zenodo.1292397.

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Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori la

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Te, Taka Keegan, and Cairns Jasmin. "Microsoft Translator Hub for Māori Language." International Journal of Computer Science Issues 15, no. 3 (2018): 8–16. https://doi.org/10.5281/zenodo.1292400.

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Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori la

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Kolesnikova, Maria Pavlovna. "The impact of CAT tools on the quality and naturalness of translation in business communications." Litera, no. 3 (March 2025): 210–20. https://doi.org/10.25136/2409-8698.2025.3.73316.

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The article examines the impact of CAT tools (Computer-Assisted Translation) on the accuracy and naturalness of translation in business communications. The paper examines the theoretical foundations of their work, linguistic aspects, methods for assessing the quality of translation, as well as cognitive effects and development prospects. The main goal is to determine to what extent automated translation systems meet the criteria of accuracy and naturalness of the text in comparison with professional translation, to identify their capabilities and limitations. The key mechanisms of CAT tools, s

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Cao, Jialun, Meiziniu Li, Yeting Li, Ming Wen, Shing-Chi Cheung, and Haiming Chen. "SemMT: A Semantic-Based Testing Approach for Machine Translation Systems." ACM Transactions on Software Engineering and Methodology 31, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3490488.

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Machine translation has wide applications in daily life. In mission-critical applications such as translating official documents, incorrect translation can have unpleasant or sometimes catastrophic consequences. This motivates recent research on the testing methodologies for machine translation systems. Existing methodologies mostly rely on metamorphic relations designed at the textual level (e.g., Levenshtein distance) or syntactic level (e.g., distance between grammar structures) to determine the correctness of translation results. However, these metamorphic relations do not consider whether

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Rivas Carmona, María del Mar. "La traduction automatique comme ressource pour la traduction humaniste-littéraire : un instrument ‘artificiel’ peut-il transférer un texte essentiellement ‘humain’ et ‘social’ ?" Studia Romanica Posnaniensia 51, no. 3 (2024): 7–20. http://dx.doi.org/10.14746/strop.2024.51.3.1.

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The usefulness of machine translators (MTs) is virtually indisputable. Although, until recently, they could be considered almost the ‘enemy’ of translators, the rise of technology in our profession has changed from a threat to an opportunity to broaden skills and take on new roles (Moorkens, 2018). MT has evolved since its inception and, in particular, Statistical MT (SMT) and Neural Network-based MT (NMT) systems have become very popular. One question that deserves particular attention is whether these automatic resources are just as effective for a humanistic-literary translation as they are

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Kim, Euigyum, and Hyo Jeong Shin. "Leveraging Large Language Model for Automatic Translation of Educational Content: Exploring the Effectiveness of Curriculum-Aware Prompt Engineering." Korean Educational Research Association 63, no. 2 (2025): 427–62. https://doi.org/10.30916/kera.63.2.427.

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Despite the globalization of educational content, language remains a significant barrier. When translating educational content, multilingual translation has become crucial to meet this challenge, with an emphasis on incorporating the cultural context of the target country and the educational context of the learners. However, existing machine translation systems often fail to adequately account for these contextual factors. This study explores the potential of the Large Language Model(LLM) to improve the translation of assessment items through In-context Learning. Two prompt engineering strateg

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Aiken, Milam, and Kaushik Ghosh. "Automatic translation in multilingual business meetings." Industrial Management & Data Systems 109, no. 7 (2009): 916–25. http://dx.doi.org/10.1108/02635570910982274.

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Śmigielska, Beata. "Traduction automatique et désambiguïsation des sens des mots. Le cas du verbe français louer." Neophilologica 2019 35 (December 29, 2023): 1–26. http://dx.doi.org/10.31261/neo.2023.35.21.

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Machine translators such as those offered by Google, Deep, ChatGPT and others are already using NMT systems. They apply the techniques of artificial intelligence that are constantly being developed, so they are currently providing increasingly correct and suitable translations. In the first part of this article, the author examines the case of the French verb louer by having it translated in different contexts by three automatic translators mentioned to see how effective they currently are as translation tools. Next, the verb louer is presented and analyzed as seen through the prism of the Ant

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Chama, Wafa, Allaoua Chaoui, and Seidali Rehab. "Formal Modeling and Analysis of Object Oriented Systems using Triple Graph Grammars." International Journal of Embedded and Real-Time Communication Systems 6, no. 2 (2015): 48–64. http://dx.doi.org/10.4018/ijertcs.2015040103.

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This paper proposes a Model Driven Engineering automatic translation approach based on the integration of rewriting logic formal specification and UML semi-formal models. This integration is a contribution in formalizing UML models since it lacks for formal semantics. It aims at providing UML with the capabilities of rewriting logic and its Maude language to control and detect incoherencies in their diagrams. Rewriting logic Maude language allows simulation and verification of system's properties using its LTL model-checker. This automatic translation approach is based on meta-modeling and gra

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Silva, Carlos Eduardo, and Lincoln Fernandes. "Apresentando o copa-trad versão 2.0 um sistema com base em corpus paralelo para pesquisa, ensino e prática da tradução." Ilha do Desterro A Journal of English Language, Literatures in English and Cultural Studies 73, no. 1 (2020): 297–316. http://dx.doi.org/10.5007/2175-8026.2020v73n1p297.

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This paper describes COPA-TRAD Version 2.0, a parallel corpus-based system developed at the Universidade Federal de Santa Catarina (UFSC) for translation research, teaching and practice. COPA-TRAD enables the user to investigate the practices of professional translators by identifying translational patterns related to a particular element or linguistic pattern. In addition, the system allows for the comparison between human translation and automatic translation provided by three well-known machine translation systems available on the Internet (Google Translate, Microsoft Translator and Yandex)

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