Relevant bibliographies by topics / Machine translation

Contents

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

Academic literature on the topic 'Machine translation'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2024

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Journal articles on the topic "Machine translation"

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He, Linli, Mozhgan Ghassemiazghandi, and Ilangko Subramaniam. "Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation." Forum for Linguistic Studies 6, no. 2 (April 22, 2024): 1189. http://dx.doi.org/10.59400/fls.v6i2.1189.

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This study explores the performance of machine translation of literary texts from English to Chinese. The study compares two machine translation systems, Bing Translator and Youdao Machine Translation, using selected texts from the novel “Nineteen eighty-four” by George Orwell. The data collection includes the original source texts, their machine-generated translations by Bing Translator and Youdao Machine Translation, and comparisons with human reference translations to assess the performance of these systems. The research’s focal point is to evaluate the accuracy, fluency, and appropriateness of translations generated by these two machine translation systems, while also analyzing the post-editing effort required to enhance the quality of the final machine-translated product. The study revealed that despite the presence of flaws in both machine translation systems, Youdao Machine Translation demonstrated superior performance, especially in accurately translating technical terms and idiomatic expressions, making it the more effective option overall. Nevertheless, the translations from Youdao Machine Translation required more substantial post-editing efforts to improve fluency and readability. Conversely, Bing Translator yielded more fluent and natural-sounding translations, albeit with a need for improved accuracy in translating technical terms and idiomatic expressions. The study concludes that while machine translation systems are capable of generating reasonable translations for literary texts, human post-editing remains essential to ensure the final output’s accuracy, fluency, and appropriateness. The study underscores the importance of selecting the appropriate machine translation system based on the nature of the text being translated. It also highlights the critical role of post-editing in refining the quality of machine-translated outputs, suggesting that while machine translation can provide a solid foundation, human intervention is indispensable for achieving optimal accuracy, fluency, and overall readability in literary translations.
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Ardi, Havid, Muhd Al Hafizh, Iftahur Rezqi, and Raihana Tuzzikriah. "CAN MACHINE TRANSLATIONS TRANSLATE HUMOROUS TEXTS?" Humanus 21, no. 1 (May 11, 2022): 99. http://dx.doi.org/10.24036/humanus.v21i1.115698.

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Machine translation (MT) have attracted many researchers’attention in various ways. Although the advanced of technology brings development to the result of MT, the quality are still criticized. One of the texts that has great challenges and translation problems is humorous text. Humorous texts that trigger a smile or laugh should have the same effect in another language. Humor uses linguistic, cultural, and universal aspects to create joke or humor. These raise questions how do machines translate humorous texts from English into Indonesian? This article aimed at comparing the translation result and error made by three prominent Machine Translations (Google Translate, Yandex Translate, and Bing Microsoft Translator) in translating humorous texts. This research applied qualitative descriptive method. The data were taken by comparing the translation results produced by 3 online Machine Translations in translating four humorous texts. The findings show that Google Translate produced better translation result. There are some errors related to lexical, syntaxis, semantics, and pragmatics errors in the. The implication of this finding shows that machine translation still need human in post editing to produce similar effect to preserve the humor.
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Xiu, Peng, and Liming Xeauyin. "Human translation vs machine translation: The practitioner phenomenology." Linguistics and Culture Review 2, no. 1 (May 9, 2018): 13–23. http://dx.doi.org/10.21744/lingcure.v2n1.8.

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The paper aimed at exploring the current phenomenon regarding human translation with machine translation. Human translation (HT), by definition, is when a human translator—rather than a machine—translate text. It's the oldest form of translation, relying on pure human intelligence to convert one way of saying things to another. The person who performs language translation. Learn more about using technology to reduce healthcare disparity. A person who performs language translation. The translation is necessary for the spread of information, knowledge, and ideas. It is absolutely necessary for effective and empathetic communication between different cultures. Translation, therefore, is critical for social harmony and peace. Only a human translation can tell the difference because the machine translator will just do the direct word to word translation. This is a hindrance to machines because they are not advanced to the level of rendering these nuances accurately, but they can only do word to word translations. There are different translation techniques, diverse theories about translation and eight different translation services types, including technical translation, judicial translation and certified translation. The translation is the process of translating the sequence of a messenger RNA (mRNA) molecule to a sequence of amino acids during protein synthesis. The genetic code describes the relationship between the sequence of base pairs in a gene and the corresponding amino acid sequence that it encodes.
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Garcia, Ignacio. "Is machine translation ready yet?" Target. International Journal of Translation Studies 22, no. 1 (June 30, 2010): 7–21. http://dx.doi.org/10.1075/target.22.1.02gar.

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The default option of the Google Translator Toolkit (GTT), released in June 2009, is to “pre-fill with machine translation” all segments for which a ‘no match’ has been returned by the memories, while the Settings window clearly advises that “[m]ost users should not modify this”. To confirm whether this approach indeed benefits translators and translation quality, we designed and performed tests whereby trainee translators used the GTT to translate passages from English into Chinese either entirely from the source text, or after seeding of empty segments by the Google Translate engine as recommended. The translations were timed, and their quality assessed by independent experienced markers following Australian NAATI test criteria. Our results show that, while time differences were not significant, the machine translation seeded passages were more favourably assessed by the markers in thirty three of fifty six cases. This indicates that, at least for certain tasks and language combinations—and against the received wisdom of translation professionals and translator trainers—translating by proofreading machine translation may be advantageous.
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Khabarova, E. M. "Machine translation of expressive means – metaphors." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (December 18, 2023): 108–19. http://dx.doi.org/10.17726/philit.2023.2.8.

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Technology has advanced significantly over the past decades. Significant changes have occurred in the field of translation with the development of programs such as Google.translate and Yandex.translator. The presented applications are already being actively implemented in translation agencies to optimize translation activities, where written translations of documents, articles, annotations, etc. must be provided to customers as quick as possible. While working with popular science text, online programs help translators gain time, but this requires to edit the text. The artistic style requires more concentration and dedication, because. the means of expression presented in it require taking into account the context and nuances of the use of certain units of language. Machine translation has the potential to become an indispensable assistant in the hands of a translator. This article discusses machine translation of expressive means, namely metaphors. The study is illustrated with examples and a comparative analysis of the translation of metaphorical units is carried out, the classification of metaphors is identified and an analysis is carried out with which means of expression the translator is able to cope with. The difficulties of translating metaphors are analyzed.
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Danylov, Hlib, Viktoriia Balakirieva, and Kateryna Vasylenko. "MACHINE TRANSLATION, MACHINE TRANSLATION SYSTEMS AND THEIR SPECIFICATIONS." Naukovy Visnyk of South Ukrainian National Pedagogical University named after K. D. Ushynsky: Linguistic Sciences 2021, no. 33 (December 2021): 293–310. http://dx.doi.org/10.24195/2616-5317-2021-33-22.

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The article is devoted to the study of machine translation, machine translation systems and their specificity. The relevance of the work is determined by fast and strong changes of auto-translators and sharply increasing, qualitatively changing needs. The aim of the study is to consider the translation of scientific and technical texts by machine translators without the participation of a professional translator. We use them in business correspondence, for informal communication with friends from other countries, on foreign trips. While having a number of advantages, they also have many disadvantages. The main problem raised in this article is whether a computer can completely replace a person. Answering this question, we come to the conclusion that such a replacement is impossible at the present stage of technology development. Machine translation is not yet capable of fully translating phraseological units and slang. The computer does not take into account the peculiarities of the context, the specifics of the construction of sentences, irony and humor. Only a person can convey all the nuances of the language, play on words, the author’s style. In some areas, even the most accurate and correct translation of a computer is subject to multiple human checks. This applies to the translation of medical topics, legal documents and texts, where the cost of an error can be very high, up to a human life. The same sad situation develops in the translation of works of art, in which, in addition to meaning, it is necessary to convey emotions, expression, imagery. In addition, the style of the work, culture, era, wordplay, humor should be preserved. Not every professional translator can do this. Even more difficult is the task of a translator working on a poetic form, where it is necessary to preserve both the meaning, and also the rhythm, tact, metaphor.
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Mohar, Tjaša, Sara Orthaber, and Tomaž Onič. "Machine Translated Atwood: Utopia or Dystopia?" ELOPE: English Language Overseas Perspectives and Enquiries 17, no. 1 (May 26, 2020): 125–41. http://dx.doi.org/10.4312/elope.17.1.125-141.

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Margaret Atwood’s masterful linguistic creativity exceeds the limits of ordinary discourse. Her elliptical language contributes to interpretative gaps, while the ambiguity and openness of her texts intentionally deceive the reader. The translator of Atwood’s texts therefore faces the challenge of identifying the rich interpretative potential of the original, as well as of preserving it in the target language. Witnessing the rise of artificial intelligence, a natural question arises whether a human translator could ever be replaced by a machine in translating such challenging texts. This article aims to contribute to the ongoing debate on literary machine translation by examining the translations of Atwood’s “Life Stories” generated by two neural machine translation (NMT) systems and comparing them to those produced by translation students. We deliberately chose a literary text where the aesthetic value depends mostly on the author’s personal style, and which we had presumed would be problematic to translate.
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Tahseen, Wesam Mohsen, and Shifa'a Hadi Hussein. "Investigating Machine Translation Errors in Rendering English Literary Texts into Arabic." Integrated Journal for Research in Arts and Humanities 4, no. 1 (January 18, 2024): 68–81. http://dx.doi.org/10.55544/ijrah.4.1.11.

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Machine translation is a machine that employs artificial intelligence (AI) to translate texts between languages without human intervention. Machine translation approaches translate text or speech from one language to another, including the contextual, idiomatic and pragmatic issues of both different languages. The present study aims to analyze the translation of literary texts selected from different novels, plays, and poems and clarify the method for translating them from English into Arabic. This study also aims to discover machine translation errors in rendering English literary texts and clarify the translator's role in transferring the rhetorical impact on the reader who reads the (TT). This study hypothesizes that translators(students) face difficulties regarding words and structures when translating literary texts from English into Arabic because they misunderstand rhetorical devices. So they tend to use machine translations that translate literally, such as (Google Translate, Reverso translation and Bing Microsoft translation). This study adopted two models: First, Newmark's translation model (1988b), which includes two basic types of translation: semantic and communicative. This model is used widely in the analysis of literary texts. Second, Harris (2018) linguistic model theory of rhetorical question and the general purpose of the rhetorical devices to analyze the data. Finally, the study ends with the conclusions that all machine translation programs (Google Translate (GT), Reverso Translation (Reverso. T), Bing Microsoft Translation (Bing. M.T) in rendering English literary texts from English into Arabic are unacceptable and have more problems because these programs are just machines and cannot think or feel as well as all these machines renderings are meaningless and ambiguous. So Human translation is better than Machine Translation because the first uses communicative translation while the other uses semantic translation.
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Abdullah H Aldawsar, Hamad. "Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms." Arab World English Journal 1, no. 1 (April 24, 2024): 237–51. http://dx.doi.org/10.24093/awej/chatgpt.16.

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This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.
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Zhang, Wanfang, and Yuan Tang. "Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment." Computational Intelligence and Neuroscience 2022 (August 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/7428563.

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With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People’s life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences.
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Dissertations / Theses on the topic "Machine translation"

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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.

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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this specific condition, MT can come into play by enabling the so-called “translation-based” workarounds. The idea is simple: first, input texts in the low-resource language are translated into a resource-rich target language; then, the machine-translated text is processed by well-trained NLP tools in the target language; finally, the output of these downstream components is projected back to the source language. This results in a new scenario, in which the end-user of MT technology is no longer a human but another machine. We hypothesize that current MT training approaches are not the optimal ones for this setting, in which the objective is to maximize the performance of a downstream tool fed with machine-translated text rather than human comprehension. Under this hypothesis, this thesis introduces a new research paradigm, which we named “MT for machines”, addressing a number of questions that raise from this novel view of the MT problem. Are there different quality criteria for humans and machines? What makes a good translation from the machine standpoint? What are the trade-offs between the two notions of quality? How to pursue machine-oriented objectives? How to serve different downstream components with a single MT system? How to exploit knowledge transfer to operate in different language settings with a single MT system? Elaborating on these questions, this thesis: i) introduces a novel and challenging MT paradigm, ii) proposes an effective method based on Reinforcement Learning analysing its possible variants, iii) extends the proposed method to multitask and multilingual settings so as to serve different downstream applications and languages with a single MT system, iv) studies the trade-off between machine-oriented and human-oriented criteria, and v) discusses the successful application of the approach in two real-world scenarios.
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Кириченко, Олена Анатоліївна, Елена Анатольевна Кириченко, Olena Anatoliivna Kyrychenko, and Y. V. Kalashnyk. "Machine translation." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/12977.

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Karlbom, Hannes. "Hybrid Machine Translation : Choosing the best translation with Support Vector Machines." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-304257.

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In the field of machine translation there are various systems available which have different strengths and weaknesses. This thesis investigates the combination of two systems, a rule based one and a statistical one, to see if such a hybrid system can provide higher quality translations. The classification approach was taken, where a support vector machine is used to choose which sentences from each of the two systems result in the best translation. To label the sentences from the collected data a new method of simulated annealing was applied and compared to previously tried heuristics. The results show that a hybrid system has an increased average BLEU score of 6.10% or 1.86 points over the single best system, and that using the labels created through simulated annealing, over heuristic rules, gives a significant improvement in classifier performance.
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Quernheim, Daniel. "Bimorphism Machine Translation." Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-223667.

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The field of statistical machine translation has made tremendous progress due to the rise of statistical methods, making it possible to obtain a translation system automatically from a bilingual collection of text. Some approaches do not even need any kind of linguistic annotation, and can infer translation rules from raw, unannotated data. However, most state-of-the art systems do linguistic structure little justice, and moreover many approaches that have been put forward use ad-hoc formalisms and algorithms. This inevitably leads to duplication of effort, and a separation between theoretical researchers and practitioners. In order to remedy the lack of motivation and rigor, the contributions of this dissertation are threefold: 1. After laying out the historical background and context, as well as the mathematical and linguistic foundations, a rigorous algebraic model of machine translation is put forward. We use regular tree grammars and bimorphisms as the backbone, introducing a modular architecture that allows different input and output formalisms. 2. The challenges of implementing this bimorphism-based model in a machine translation toolkit are then described, explaining in detail the algorithms used for the core components. 3. Finally, experiments where the toolkit is applied on real-world data and used for diagnostic purposes are described. We discuss how we use exact decoding to reason about search errors and model errors in a popular machine translation toolkit, and we compare output formalisms of different generative capacity.
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Caglayan, Ozan. "Multimodal Machine Translation." Thesis, Le Mans, 2019. http://www.theses.fr/2019LEMA1016/document.

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La traduction automatique vise à traduire des documents d’une langue à une autre sans l’intervention humaine. Avec l’apparition des réseaux de neurones profonds (DNN), la traduction automatique neuronale(NMT) a commencé à dominer le domaine, atteignant l’état de l’art pour de nombreuses langues. NMT a également ravivé l’intérêt pour la traduction basée sur l’interlangue grâce à la manière dont elle place la tâche dans un cadre encodeur-décodeur en passant par des représentations latentes. Combiné avec la flexibilité architecturale des DNN, ce cadre a aussi ouvert une piste de recherche sur la multimodalité, ayant pour but d’enrichir les représentations latentes avec d’autres modalités telles que la vision ou la parole, par exemple. Cette thèse se concentre sur la traduction automatique multimodale(MMT) en intégrant la vision comme une modalité secondaire afin d’obtenir une meilleure compréhension du langage, ancrée de façon visuelle. J’ai travaillé spécifiquement avec un ensemble de données contenant des images et leurs descriptions traduites, où le contexte visuel peut être utile pour désambiguïser le sens des mots polysémiques, imputer des mots manquants ou déterminer le genre lors de la traduction vers une langue ayant du genre grammatical comme avec l’anglais vers le français. Je propose deux approches principales pour intégrer la modalité visuelle : (i) un mécanisme d’attention multimodal qui apprend à prendre en compte les représentations latentes des phrases sources ainsi que les caractéristiques visuelles convolutives, (ii) une méthode qui utilise des caractéristiques visuelles globales pour amorcer les encodeurs et les décodeurs récurrents. Grâce à une évaluation automatique et humaine réalisée sur plusieurs paires de langues, les approches proposées se sont montrées bénéfiques. Enfin,je montre qu’en supprimant certaines informations linguistiques à travers la dégradation systématique des phrases sources, la véritable force des deux méthodes émerge en imputant avec succès les noms et les couleurs manquants. Elles peuvent même traduire lorsque des morceaux de phrases sources sont entièrement supprimés
Machine translation aims at automatically translating documents from one language to another without human intervention. With the advent of deep neural networks (DNN), neural approaches to machine translation started to dominate the field, reaching state-ofthe-art performance in many languages. Neural machine translation (NMT) also revived the interest in interlingual machine translation due to how it naturally fits the task into an encoder-decoder framework which produces a translation by decoding a latent source representation. Combined with the architectural flexibility of DNNs, this framework paved the way for further research in multimodality with the objective of augmenting the latent representations with other modalities such as vision or speech, for example. This thesis focuses on a multimodal machine translation (MMT) framework that integrates a secondary visual modality to achieve better and visually grounded language understanding. I specifically worked with a dataset containing images and their translated descriptions, where visual context can be useful forword sense disambiguation, missing word imputation, or gender marking when translating from a language with gender-neutral nouns to one with grammatical gender system as is the case with English to French. I propose two main approaches to integrate the visual modality: (i) a multimodal attention mechanism that learns to take into account both sentence and convolutional visual representations, (ii) a method that uses global visual feature vectors to prime the sentence encoders and the decoders. Through automatic and human evaluation conducted on multiple language pairs, the proposed approaches were demonstrated to be beneficial. Finally, I further show that by systematically removing certain linguistic information from the input sentences, the true strength of both methods emerges as they successfully impute missing nouns, colors and can even translate when parts of the source sentences are completely removed
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Wang, Long Qi. "Translation accuracy comparison between machine translation and context-free machine natural language grammar–based translation." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950657.

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Davis, Paul C. "Stone Soup Translation: The Linked Automata Model." Connect to this title online, 2002. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1023806593.

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Thesis (Ph. D.)--Ohio State University, 2002.
Title from first page of PDF file. Document formatted into pages; contains xvi, 306 p.; includes graphics. Includes abstract and vita. Advisor: Chris Brew, Dept. of Linguistics. Includes indexes. Includes bibliographical references (p. 284-293).
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Sim, Smith Karin M. "Coherence in machine translation." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20083/.

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Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and with no access to extra-sentential context. In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as evidenced via discourse relations. For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text.
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Sato, Satoshi. "Example-Based Machine Translation." Kyoto University, 1992. http://hdl.handle.net/2433/154652.

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本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである
Kyoto University (京都大学)
0048
新制・論文博士
博士(工学)
乙第7735号
論工博第2539号
新制||工||860(附属図書館)
UT51-92-B162
(主査)教授 長尾 真, 教授 堂下 修司, 教授 池田 克夫
学位規則第4条第2項該当
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García, Martínez Mercedes. "Factored neural machine translation." Thesis, Le Mans, 2018. http://www.theses.fr/2018LEMA1002/document.

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La diversité des langues complexifie la tâche de communication entre les humains à travers les différentes cultures. La traduction automatique est un moyen rapide et peu coûteux pour simplifier la communication interculturelle. Récemment, laTraduction Automatique Neuronale (NMT) a atteint des résultats impressionnants. Cette thèse s'intéresse à la Traduction Automatique Neuronale Factorisé (FNMT) qui repose sur l'idée d'utiliser la morphologie et la décomposition grammaticale des mots (lemmes et facteurs linguistiques) dans la langue cible. Cette architecture aborde deux défis bien connus auxquelles les systèmes NMT font face. Premièrement, la limitation de la taille du vocabulaire cible, conséquence de la fonction softmax, qui nécessite un calcul coûteux à la couche de sortie du réseau neuronale, conduisant à un taux élevé de mots inconnus. Deuxièmement, le manque de données adéquates lorsque nous sommes confrontés à un domaine spécifique ou une langue morphologiquement riche. Avec l'architecture FNMT, toutes les inflexions des mots sont prises en compte et un vocabulaire plus grand est modélisé tout en gardant un coût de calcul similaire. De plus, de nouveaux mots non rencontrés dans les données d'entraînement peuvent être générés. Dans ce travail, j'ai développé différentes architectures FNMT en utilisant diverses dépendances entre les lemmes et les facteurs. En outre, j'ai amélioré la représentation de la langue source avec des facteurs. Le modèle FNMT est évalué sur différentes langues dont les plus riches morphologiquement. Les modèles à l'état de l'art, dont certains utilisant le Byte Pair Encoding (BPE) sont comparés avec le modèle FNMT en utilisant des données d'entraînement de petite et de grande taille. Nous avons constaté que les modèles utilisant les facteurs sont plus robustes aux conditions d'entraînement avec des faibles ressources. Le FNMT a été combiné avec des unités BPE permettant une amélioration par rapport au modèle FNMT entrainer avec des données volumineuses. Nous avons expérimenté avec dfférents domaines et nous avons montré des améliorations en utilisant les modèles FNMT. De plus, la justesse de la morphologie est mesurée à l'aide d'un ensemble de tests spéciaux montrant l'avantage de modéliser explicitement la morphologie de la cible. Notre travail montre les bienfaits de l'applicationde facteurs linguistiques dans le NMT
Communication between humans across the lands is difficult due to the diversity of languages. Machine translation is a quick and cheap way to make translation accessible to everyone. Recently, Neural Machine Translation (NMT) has achievedimpressive results. This thesis is focus on the Factored Neural Machine Translation (FNMT) approach which is founded on the idea of using the morphological and grammatical decomposition of the words (lemmas and linguistic factors) in the target language. This architecture addresses two well-known challenges occurring in NMT. Firstly, the limitation on the target vocabulary size which is a consequence of the computationally expensive softmax function at the output layer of the network, leading to a high rate of unknown words. Secondly, data sparsity which is arising when we face a specific domain or a morphologically rich language. With FNMT, all the inflections of the words are supported and larger vocabulary is modelled with similar computational cost. Moreover, new words not included in the training dataset can be generated. In this work, I developed different FNMT architectures using various dependencies between lemmas and factors. In addition, I enhanced the source language side also with factors. The FNMT model is evaluated on various languages including morphologically rich ones. State of the art models, some using Byte Pair Encoding (BPE) are compared to the FNMT model using small and big training datasets. We found out that factored models are more robust in low resource conditions. FNMT has been combined with BPE units performing better than pure FNMT model when trained with big data. We experimented with different domains obtaining improvements with the FNMT models. Furthermore, the morphology of the translations is measured using a special test suite showing the importance of explicitly modeling the target morphology. Our work shows the benefits of applying linguistic factors in NMT
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Books on the topic "Machine translation"

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Christa, Hauenschild, and Heizmann Susanne 1963-, eds. Machine translation and translation theory. Berlin: Mouton de Gruyter, 1997.

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Su, Jinsong, and Rico Sennrich, eds. Machine Translation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7512-6.

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Shi, Xiaodong, and Yidong Chen, eds. Machine Translation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45701-6.

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Chen, Jiajun, and Jiajun Zhang, eds. Machine Translation. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3083-4.

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Yang, Muyun, and Shujie Liu, eds. Machine Translation. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3635-4.

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Huang, Shujian, and Kevin Knight, eds. Machine Translation. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1721-1.

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Wong, Derek F., and Deyi Xiong, eds. Machine Translation. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7134-8.

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Li, Junhui, and Andy Way, eds. Machine Translation. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6162-1.

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Xiao, Tong, and Juan Pino, eds. Machine Translation. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7960-6.

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Feng, Yang, and Chong Feng, eds. Machine Translation. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7894-6.

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Book chapters on the topic "Machine translation"

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Chiang, David. "Machine Translation." In Grammars for Language and Genes, 51–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20444-9_4.

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Weik, Martin H. "machine translation." In Computer Science and Communications Dictionary, 952. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10823.

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Tsujii, Junichi. "Machine translation." In Recent Advances in Natural Language Processing, 377. Amsterdam: John Benjamins Publishing Company, 1997. http://dx.doi.org/10.1075/cilt.136.32tsu.

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Way, Andy. "Machine Translation." In The Handbook of Computational Linguistics and Natural Language Processing, 531–73. Oxford, UK: Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444324044.ch19.

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Brown, Antony F. R. "Machine translation." In Studies in the History of the Language Sciences, 129. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/sihols.97.12bro.

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Marcuk, Jurij N. "Machine translation." In Studies in the History of the Language Sciences, 243. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/sihols.97.22mar.

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Kenny, Dorothy. "Machine translation." In Routledge Encyclopedia of Translation Studies, 305–10. 3rd ed. Third edition. | London ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781315678627-65.

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Bowker, Lynne. "Machine translation." In De-mystifying Translation, 92–110. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003217718-7.

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Wang, Peng, and David B. Sawyer. "Machine Translation." In Machine Learning in Translation, 71–91. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003321538-7.

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Hutchins, W. John. "Machine Translation." In Routledge Encyclopedia of Translation Technology, 128–44. 2nd ed. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003168348-7.

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Conference papers on the topic "Machine translation"

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Mirkin, Shachar, and Jean-Luc Meunier. "Personalized Machine Translation: Predicting Translational Preferences." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1238.

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Wang, Xing, Zhaopeng Tu, Deyi Xiong, and Min Zhang. "Translating Phrases in Neural Machine Translation." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1149.

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Rolf, P. C. "Machine translation." In the 12th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1988. http://dx.doi.org/10.3115/991719.991751.

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XU, Jitao, Josep Crego, and Jean Senellart. "Boosting Neural Machine Translation with Similar Translations." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.144.

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Meng, Fandong, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang, and Di Wang. "Neural Machine Translation with Key-Value Memory-Augmented Attention." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/357.

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Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations. To alleviate these issues, we propose a novel key-value memory-augmented attention model for NMT, called KVMEMATT. Specifically, we maintain a timely updated keymemory to keep track of attention history and a fixed value-memory to store the representation of source sentence throughout the whole translation process. Via nontrivial transformations and iterative interactions between the two memories, the decoder focuses on more appropriate source word(s) for predicting the next target word at each decoding step, therefore can improve the adequacy of translations. Experimental results on Chinese)English and WMT17 German,English translation tasks demonstrate the superiority of the proposed model.
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Salunkhe, Pramod, Aniket D. Kadam, Shashank Joshi, Shuhas Patil, Devendrasingh Thakore, and Shrikant Jadhav. "Hybrid machine translation for English to Marathi: A research evaluation in Machine Translation: (Hybrid translator)." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7754822.

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Singh, Muskaan, Ravinder Kumar, and Inderveer Chana. "Improving Neural Machine Translation Using Rule-Based Machine Translation." In 2019 7th International Conference on Smart Computing & Communications (ICSCC). IEEE, 2019. http://dx.doi.org/10.1109/icscc.2019.8843685.

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Fadeil Alawneh, Mouiad, and Tengku Mohd. "Hybrid-Based Machine Translation Systems." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1517.

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Machine Translation (MT) is described as the method by which computer software is employed to convert text from one natural language to the other. This process includes taking into consideration each language's grammatical framework and applying examples, rules, as well as grammatical principles to adapt the grammatical structure from the source language (SL) to the target language (TL). In this paper, a method for translating well-formed English sentences into coherent Arabic sentences is introduced, utilizing grammar-based as well as example-based translation techniques to address issues related to word order and grammatical agreement. The methodology suggested is both adaptable and capable of being expanded. The primary benefits include: firstly, a hybrid approach merges the strengths of rule-based (RBMT) as well as example-based (EBMT) methodologies. Secondly, it offers the flexibility to adapt to various languages with only slight adjustments. The OAK Parser analyzes incoming English text to identify the part of speech (POS) for each word, serving as an initial step in translation, utilizing the C# programming language. To maintain data integrity, validation rules are implemented in both the database architecture as well as the programming. A key objective for this system is its capability to function independently, including its seamless integration with broader MT systems for English sentences.
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Zhang, Yi, Jing Zhao, and Shiliang Sun. "Diverse Machine Translation with Translation Memory." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892899.

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Collier, Nigel, Hideki Hirakawa, and Akira Kumano. "Machine translation vs. dictionary term translation." In the 17th international conference. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/980451.980888.

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Reports on the topic "Machine translation"

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Morgan, John J. Project-specific Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada554967.

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Hobbs, Jerry R., and Megumi Kameyama. Machine Translation Using Abductive Inference. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada259458.

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Dorr, Bonnie J. Principle-Based Parsing for Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada199183.

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Church, Kenneth W., and Eduard H. Hovy. Good Applications for Crummy Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada278689.

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Lee, Young-Suk. Morphological Analysis for Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada460276.

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Lopez, Adam. A Survey of Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada466330.

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Agnihotri, Souparni. Hyperparameter Optimization on Neural Machine Translation. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-852.

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Turian, Joseph P., Luke Shea, and I. D. Melamed. Evaluation of Machine Translation and its Evaluation. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada453509.

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Russo-Lassner, Grazia, Jimmy Lin, and Philip Resnik. A Paraphrase-Based Approach to Machine Translation Evaluation. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada448032.

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Germann, Ulrich, Michael Jahr, Kevin Knight, Daniel Marcu, and Kenji Yamada. Fast Decoding and Optimal Decoding for Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada459945.

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