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Relevant bibliographies by topics / Machine-translated

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Academic literature on the topic 'Machine-translated'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Machine-translated"

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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 (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 articl

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Dolamic, Ljiljana, and Jacques Savoy. "Retrieval effectiveness of machine translated queries." Journal of the American Society for Information Science and Technology 61, no. 11 (2010): 2266–73. http://dx.doi.org/10.1002/asi.21337.

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YOSHIMI, TAKEHIKO. "Quantitative Analysis of Morpholexical Difference between Human-Translated and Machine-Translated Sentences." Journal of Natural Language Processing 10, no. 5 (2003): 55–74. http://dx.doi.org/10.5715/jnlp.10.5_55.

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Kuo, Chen-li. "Function words in statistical machine-translated Chinese and original Chinese: A study into the translationese of machine translation systems." Digital Scholarship in the Humanities 34, no. 4 (2018): 752–71. http://dx.doi.org/10.1093/llc/fqy050.

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Abstract Statistical approaches have become the mainstream in machine translation (MT), for their potential in producing less rigid and more natural translations than rule-based approaches. However, on closer examination, the uses of function words between statistical machine-translated Chinese and the original Chinese are different, and such differences may be associated with translationese as discussed in translation studies. This article examines the distribution of Chinese function words in a comparable corpus consisting of MTs and the original Chinese texts extracted from Wikipedia. An at

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Suprato, Djuria. "Perbandingan Hasil Penerjemahan Buku Cerita Anak-Anak Dongeng Danau Toba dari Bahasa Indonesia ke Bahasa Inggris Melalui Penerjemah dan Mesin Penerjemah." Humaniora 5, no. 2 (2014): 1075. http://dx.doi.org/10.21512/humaniora.v5i2.3223.

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Machine translation is one of the technological advances that can help the translator to do the translation functions easier and faster. The purpose of this article is to analyze whether the machine translation can be relied upon as a substitute translator. From the analysis, it can be concluded that the existing machine translation cannot replace a translator because of its limitations, especially on understanding the context and cultural situation in a nation. Therefore, this machine is only in assisting the process of translation. The method used is the literature analysis. It analyzes the

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YOSHIMI, TAKEHIKO. "Comparative Analysis of Familiarity Rating of Verbs in Human-Translated and Machine-Translated Sentences." Journal of Natural Language Processing 11, no. 2 (2004): 101–13. http://dx.doi.org/10.5715/jnlp.11.2_101.

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Lembersky, Gennadi, Noam Ordan, and Shuly Wintner. "Language Models for Machine Translation: Original vs. Translated Texts." Computational Linguistics 38, no. 4 (2012): 799–825. http://dx.doi.org/10.1162/coli_a_00111.

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We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language. Corroborating established observations of Translation Studies, we demonstrate that the latter are significantly better predictors of translated sentences than the former, and hence fit the reference set better. Furthermore, translated texts yield better language models for statistical machine translation than original texts.

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Chon, Yuah V., and Dongkwang Shin. "Direct Writing, Translated Writing, and Machine-Translated Writing: A Text Level Analysis with Coh-Metrix." ENGLISH TEACHING 75, no. 1 (2020): 25–48. http://dx.doi.org/10.15858/engtea.75.1.202003.25.

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Zheng, Zaixiang, Hao Zhou, Shujian Huang, et al. "Modeling Past and Future for Neural Machine Translation." Transactions of the Association for Computational Linguistics 6 (December 2018): 145–57. http://dx.doi.org/10.1162/tacl_a_00011.

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Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism that separates the source information into two parts: translated Past contents and untranslated Future contents, which are modeled by two additional recurrent layers. The Past and Future contents are fed to both the attention model and the decoder states, which provides Neural Machine Translation (NMT) systems with the knowledge of translated and untranslated contents. Experimental results show that the pr

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Hu, Ke, Sharon O’Brien, and Dorothy Kenny. "A reception study of machine translated subtitles for MOOCs." Perspectives 28, no. 4 (2019): 521–38. http://dx.doi.org/10.1080/0907676x.2019.1595069.

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Dissertations / Theses on the topic "Machine-translated"

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Bystrova-McIntyre, Tatyana. "Cohesion in Translation: A Corpus Study of Human-translated, Machine-translated, and Non-translated Texts (Russian into English)." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353451112.

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Askarieh, Sona. "Cohesion and Comprehensibility in Swedish-English Machine Translated Texts." Thesis, Linköpings universitet, Institutionen för kultur och kommunikation, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108468.

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Access to various texts in different languages causes an increasing demand for fast, multi-purpose, and cheap translators. Pervasive internet use intensifies the necessity for intelligent and cheap translators, since traditional translation methods are excessively slow to translate different texts. During the past years, scientists carried out much research in order to add human and artificial intelligence into the old machine translation systems and the idea of developing a machine translation system came into existence during the days of World War (Kohenn, 2010). The new invention was useful

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Weiss, Sandra. "Cohesion and Comprehensibility in Polish-English Machine Translated Texts." Thesis, Linköpings universitet, Institutionen för kultur och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-74473.

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This paper is a study of Polish-English machine translation, where the impact of various types of errors on cohesion and comprehensibility of the translations was investigated. The following phenomena were in focus: 1. The most common errors produced by current state-of-the-art MT systems for Polish-English MT. 2. The effect of various types of errors on text cohesion. 3. The effect of various types of errors on readers’ understanding of the translation.

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Ilisei, Iustina-Narcisa. "A machine learning approach to the identification of translational language : an inquiry into translationese learning models." Thesis, University of Wolverhampton, 2012. http://hdl.handle.net/2436/299371.

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In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are n

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Testa, Ingrid. "Quality in human post-editing of machine-translated texts : error annotation and linguistic specifications for tackling register errors." Master's thesis, 2018. http://hdl.handle.net/10451/36289.

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During the last decade, machine translation has played an important role in the translation market and has become an essential tool for speeding up the translation process and for reducing the time and costs needed. Nevertheless, the quality of the results obtained is not completely satisfactory, as it is considerably variable, depending on numerous factors. Given this, it is necessary to combine MT with human intervention, by post-editing the machine-translated texts, in order to reach high-quality translations. This work aims at describing the MT process provided by Unbabel, a Portuguese st

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Book chapters on the topic "Machine-translated"

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Rikters, Matīss, and Inguna Skadiņa. "Combining Machine Translated Sentence Chunks from Multiple MT Systems." In Computational Linguistics and Intelligent Text Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75487-1_3.

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Vold, Eva Thue. "Using Machine-Translated Texts to Generate L3 Learners’ Metalinguistic Talk." In Metacognition in Language Learning and Teaching. Routledge, 2018. http://dx.doi.org/10.4324/9781351049146-5.

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Yasuoka, Mika, and Pernille Bjørn. "Difficulties in Establishing Local Language in Machine-Translated Mediated Communication." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22766-0_6.

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Abeustanova, Assem, and Ualsher Tukeyev. "Automatic Post-editing of Kazakh Sentences Machine Translated from English." In Advanced Topics in Intelligent Information and Database Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56660-3_25.

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Saleh, Shadi, and Pavel Pecina. "Reranking Hypotheses of Machine-Translated Queries for Cross-Lingual Information Retrieval." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44564-9_5.

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Carter, Dave, and Diana Inkpen. "Searching for Poor Quality Machine Translated Text: Learning the Difference between Human Writing and Machine Translations." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30353-1_5.

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Reeder, Flo, and Dan Loehr. "Finding the Right Words: An Analysis of Not-Translated Words in Machine Translation." In Machine Translation and the Information Soup. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49478-2_32.

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Luckert, Michael, Mortiz Schaefer-Kehnert, Welf Löwe, Morgan Ericsson, and Anna Wingkvist. "A Classifier to Determine Whether a Document is Professionally or Machine Translated." In Lecture Notes in Business Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45321-7_24.

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Jimenez, Sergio, and George Dueñas. "LAR-WordNet: A Machine-Translated, Pan-Hispanic and Regional WordNet for Spanish." In Advances in Artificial Intelligence - IBERAMIA 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03928-8_32.

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Tekwa, Kizito. "Real-Time Machine-Translated Instant Messaging: A Brief Overview with Implications for Translator Training." In Translation Education. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7390-3_9.

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Conference papers on the topic "Machine-translated"

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Seljan, S., I. Dunder, and M. Pavlovski. "Human Quality Evaluation of Machine-Translated Poetry." In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2020. http://dx.doi.org/10.23919/mipro48935.2020.9245436.

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Nguyen-Son, Hoang-Quoc, Thao Tran Phuong, Seira Hidano, and Shinsaku Kiyomoto. "Detecting Machine-Translated Text using Back Translation." In Proceedings of the 12th International Conference on Natural Language Generation. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-8626.

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Bystrova, Tatyana. "Cohesion in Non-translated, Human-translated, and Machine-translated Newspaper Editorials A Corpus-Based Study from Russian into English." In 6th Annual International Conference on Language, Literature and Linguistics (L3 2017). Global Science & Technology Forum (GSTF), 2017. http://dx.doi.org/10.5176/2251-3566_l317.75.

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Refaee, Eshrag, and Verena Rieser. "Benchmarking Machine Translated Sentiment Analysis for Arabic Tweets." In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/n15-2010.

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Khatri, Jyotsana, and Pushpak Bhattacharyya. "Filtering Back-Translated Data in Unsupervised Neural Machine Translation." In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.383.

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Khatri, Jyotsana, and Pushpak Bhattacharyya. "Filtering Back-Translated Data in Unsupervised Neural Machine Translation." In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.383.

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Abadani, Negin, Jamshid Mozafari, Afsaneh Fatemi, Mohammd Ali Nematbakhsh, and Arefeh Kazemi. "ParSQuAD: Machine Translated SQuAD dataset for Persian Question Answering." In 2021 7th International Conference on Web Research (ICWR). IEEE, 2021. http://dx.doi.org/10.1109/icwr51868.2021.9443126.

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Sazzed, Salim, and Sampath Jayarathna. "A Sentiment Classification in Bengali and Machine Translated English Corpus." In 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2019. http://dx.doi.org/10.1109/iri.2019.00029.

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Aharoni, Roee, Moshe Koppel, and Yoav Goldberg. "Automatic Detection of Machine Translated Text and Translation Quality Estimation." In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-2048.

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Lee, John, Ming Zhou, and Xiaohua Liu. "Detection of non-native sentences using machine-translated training data." In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers. Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1614108.1614132.

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