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Статті в журналах з теми "Machine translations"
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.
Повний текст джерелаJiang, Yue, and Jiang Niu. "A corpus-based search for machine translationese in terms of discourse coherence." Across Languages and Cultures 23, no. 2 (November 7, 2022): 148–66. http://dx.doi.org/10.1556/084.2022.00182.
Повний текст джерелаHalimah, Halimah. "COMPARISON OF HUMAN TRANSLATION WITH GOOGLE TRANSLATION OF IMPERATIVE SENTENCES IN PROCEDURES TEXT." BAHTERA : Jurnal Pendidikan Bahasa dan Sastra 17, no. 1 (January 31, 2018): 11–29. http://dx.doi.org/10.21009/bahtera.171.2.
Повний текст джерелаWang, Lan. "The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals." SHS Web of Conferences 163 (2023): 02021. http://dx.doi.org/10.1051/shsconf/202316302021.
Повний текст джерелаPersaud, Ajax, and Steven O'Brien. "Quality and Acceptance of Crowdsourced Translation of Web Content." International Journal of Technology and Human Interaction 13, no. 1 (January 2017): 100–115. http://dx.doi.org/10.4018/ijthi.2017010106.
Повний текст джерелаTímea Kovács. "A Comparative Analysis of the Use of ‘Thereof’ in an English Non-translated Text and the English Machine- and Human-translated Versions of the Hungarian Criminal Code." International Journal of Law, Language & Discourse 10, no. 2 (October 14, 2022): 43–54. http://dx.doi.org/10.56498/1022022411.
Повний текст джерелаLuo, Jinru, and Dechao Li. "Universals in machine translation?" International Journal of Corpus Linguistics 27, no. 1 (February 14, 2022): 31–58. http://dx.doi.org/10.1075/ijcl.19127.luo.
Повний текст джерелаAl-Shalabi, Riyad, Ghassan Kanaan, Huda Al-Sarhan, Alaa Drabsh, and Islam Al-Husban. "Evaluating Machine Translations from Arabic into English and Vice Versa." International Research Journal of Electronics and Computer Engineering 3, no. 2 (June 24, 2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.2.01.
Повний текст джерелаPathak, Amarnath, and Partha Pakray. "Neural Machine Translation for Indian Languages." Journal of Intelligent Systems 28, no. 3 (July 26, 2019): 465–77. http://dx.doi.org/10.1515/jisys-2018-0065.
Повний текст джерелаWang, Yiren, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu. "Non-Autoregressive Machine Translation with Auxiliary Regularization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5377–84. http://dx.doi.org/10.1609/aaai.v33i01.33015377.
Повний текст джерелаДисертації з теми "Machine translations"
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.
Повний текст джерелаTirnauca, Catalin Ionut. "Syntax-directed translations, tree transformations and bimorphisms." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/381246.
Повний текст джерелаLa traducción basada en la sintaxis surgió en el ámbito de la traducción automática de los lenguajes naturales. Los sistemas deben modelar las transformaciones de árboles, reordenar partes de oraciones, ser simétricos y poseer propiedades como la composición o simetría. Existen varias maneras de definir transformaciones de árboles: gramáticas síncronas, transductores de árboles y bimorfismos de árboles. Las gramáticas síncronas hacen todo tipo de rotaciones, pero las propiedades matemáticas son más difíciles de probar. Los transductores de árboles son operacionales y fáciles de implementar pero las clases principales no son cerradas bajo la composición. Los bimorfismos de árboles son difíciles de implementar, pero proporcionan una herramienta natural para probar composición o simetría. Para mejorar el proceso de traducción, las gramáticas síncronas se relacionan con los bimorfismos de árboles y con los transductores de árboles. En esta tesis se lleva a cabo un amplio estudio de la teoría y las propiedades de los sistemas de traducción dirigidas por la sintaxis, desde estas tres perspectivas muy diferentes que se complementan perfectamente entre sí: como dispositivos generativos (gramáticas síncronas), como máquinas aceptadores (transductores) y como estructuras algebraicas (bimorfismos). Se investigan y comparan al nivel de la transformación de árboles y como dispositivos que definen translaciones. El estudio se centra en bimorfismos, con especial énfasis en sus aplicaciones para el procesamiento del lenguaje natural. También se propone una completa y actualizada visión general sobre las clases de transformaciones de árboles definidos por bimorfismos, vinculándolos con los tipos conocidos de gramáticas síncronas y transductores de árboles. Probamos o recordamos todas las propiedades interesantes que tales clases poseen, mejorando así los previos conocimientos matemáticos. Además, se exponen las relaciones de inclusión entre las principales clases de bimorfismos a través de un diagrama Hasse, como dispositivos de traducción y como mecanismos de transformación de árboles.
Syntax-based machine translation was established by the demanding need of systems used in practical translations between natural languages. Such systems should, among others, model tree transformations, re-order parts of sentences, be symmetric and possess composability or forward and backward application. There are several formal ways to define tree transformations: synchronous grammars, tree transducers and tree bimorphisms. The synchronous grammars do all kind of rotations, but mathematical properties are harder to prove. The tree transducers are operational and easy to implement, but closure under composition does not hold for the main types. The tree bimorphisms are difficult to implement, but they provide a natural tool for proving composability or symmetry. To improve the translation process, synchronous grammars were related to tree bimorphisms and tree transducers. Following this lead, we give a comprehensive study of the theory and properties of syntax-directed translation systems seen from these three very different perspectives that perfectly complement each other: as generating devices (synchronous grammars), as acceptors (transducer machines) and as algebraic structures (bimorphisms). They are investigated and compared both as tree transformation and translation defining devices. The focus is on bimorphisms as they only recently got again into the spotlight especially given their applications to natural language processing. Moreover, we propose a complete and up-to-date overview on tree transformations classes defined by bimorphisms, linking them with well-known types of synchronous grammars and tree transducers. We prove or recall all the interesting properties such classes possess improving thus the mathematical knowledge on synchronous grammars and/or tree transducers. Also, inclusion relations between the main classes of bimorphisms both as translation devices and as tree transformation mechanisms are given for the first time through a Hasse diagram. Directions for future work are suggested by exhibiting how to extend previous results to more general classes of bimorphisms and synchronous grammars.
Al, Batineh Mohammed S. "Latent Semantic Analysis, Corpus stylistics and Machine Learning Stylometry for Translational and Authorial Style Analysis: The Case of Denys Johnson-Davies’ Translations into English." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1429300641.
Повний текст джерелаTebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Повний текст джерелаTiedemann, Jörg. "Recycling Translations : Extraction of Lexical Data from Parallel Corpora and their Application in Natural Language Processing." Doctoral thesis, Uppsala University, Department of Linguistics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3791.
Повний текст джерелаThe focus of this thesis is on re-using translations in natural language processing. It involves the collection of documents and their translations in an appropriate format, the automatic extraction of translation data, and the application of the extracted data to different tasks in natural language processing.
Five parallel corpora containing more than 35 million words in 60 languages have been collected within co-operative projects. All corpora are sentence aligned and parts of them have been analyzed automatically and annotated with linguistic markup.
Lexical data are extracted from the corpora by means of word alignment. Two automatic word alignment systems have been developed, the Uppsala Word Aligner (UWA) and the Clue Aligner. UWA implements an iterative "knowledge-poor" word alignment approach using association measures and alignment heuristics. The Clue Aligner provides an innovative framework for the combination of statistical and linguistic resources in aligning single words and multi-word units. Both aligners have been applied to several corpora. Detailed evaluations of the alignment results have been carried out for three of them using fine-grained evaluation techniques.
A corpus processing toolbox, Uplug, has been developed. It includes the implementation of UWA and is freely available for research purposes. A new version, Uplug II, includes the Clue Aligner. It can be used via an experimental web interface (UplugWeb).
Lexical data extracted by the word aligners have been applied to different tasks in computational lexicography and machine translation. The use of word alignment in monolingual lexicography has been investigated in two studies. In a third study, the feasibility of using the extracted data in interactive machine translation has been demonstrated. Finally, extracted lexical data have been used for enhancing the lexical components of two machine translation systems.
Joelsson, Jakob. "Translationese and Swedish-English Statistical Machine Translation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305199.
Повний текст джерелаKarlbom, 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.
Повний текст джерелаAhmadniaye, Bosari Benyamin. "Reliable training scenarios for dealing with minimal parallel-resource language pairs in statistical machine translation." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/461204.
Повний текст джерелаThe thesis is about the topic of high-quality Statistical Machine Translation (SMT) systems for working with minimal parallel-resource language pairs entitled “Reliable Training Scenarios for Dealing with Minimal Parallel-Resource Language Pairs in Statistical Machine Translation”. Then main challenge we targeted in our approaches is parallel data scarcity, and this challenge is faced in different solution scenarios. SMT is one of the preferred approaches to Machine Translation (MT), and various improvements could be detected in this approach, specifically in the output quality in a number of systems for language pairs since the advances in computational power, together with the exploration of new methods and algorithms have been made. When we ponder over the development of SMT systems for many language pairs, the major bottleneck that we will find is the lack of training parallel data. Due to the fact that lots of time and effort is required to create these corpora, they are available in limited quantity, genre, and language. SMT models learn that how they could do translation through the process of examining a bilingual parallel corpus that contains the sentences aligned with their human-produced translations. However, the output quality of SMT systems is heavily dependent on the availability of massive amounts of parallel text within the source and target languages. Hence, an important role is played by the parallel resources so that the quality of SMT systems could be improved. We define minimal parallel-resource SMT settings possess only small amounts of parallel data, which can also be seen in various pairs of languages. The performance achieved by current state-of-the-art minimal parallel-resource SMT is highly appreciable, but they usually use the monolingual text and do not fundamentally address the shortage of parallel training text. Creating enlargement in the parallel training data without providing any sort of guarantee on the quality of the bilingual sentence pairs that have been newly generated, is also raising concerns. The limitations that emerge during the training of the minimal parallel- resource SMT prove that the current systems are incapable of producing the high- quality translation output. In this thesis, we have proposed the “direct-bridge combination” scenario as well as the “round-trip training” scenario, that the former is based on bridge language technique while the latter one is based on retraining approach, for dealing with minimal parallel-resource SMT systems. Our main aim for putting forward the direct-bridge combination scenario is that we might bring it closer to state-of-the-art performance. This scenario has been proposed to maximize the information gain by choosing the appropriate portions of the bridge-based translation system that do not interfere with the direct translation system which is trusted more. Furthermore, the round-trip training scenario has been proposed to take advantage of the readily available generated bilingual sentence pairs to build high-quality SMT system in an iterative behavior; by selecting high- quality subset of generated sentence pairs in target side, preparing their suitable correspond source sentences, and using them together with the original sentence pairs to retrain the SMT system. The proposed methods are intrinsically evaluated, and their comparison is made against the baseline translation systems. We have also conducted the experiments in the aforementioned proposed scenarios with minimal initial bilingual data. We have demonstrated improvement made in the performance through the use of proposed methods while building high-quality SMT systems over the baseline involving each scenario.
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.
Повний текст джерелаTitle 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).
Martínez, Garcia Eva. "Document-level machine translation : ensuring translational consistency of non-local phenomena." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668473.
Повний текст джерелаEn esta tesis se estudia la traducción automática de documentos teniendo en cuenta fenómenos que ocurren entre oraciones. Típicamente, esta información a nivel de documento se ignora por la mayoría de los sistemas de Traducción Automática (MT), que se centran en traducir los textos procesando cada una de las frases que los componen de manera aislada. Traducir cada frase sin mirar al contexto que la rodea puede llevar a generar cierto tipo de errores de traducción, como pueden ser traducciones inconsistentes para la misma palabra o para elementos que aparecen en la misma cadena de correferencia. En este trabajo se presentan métodos para prestar atención a fenómenos a nivel de documento con el objetivo de evitar este tipo de errores y así llegar a generar traducciones que transmitan correctamente el significado original del texto. Nuestra investigación empieza por identificar los errores de traducción relacionados con los fenómenos a nivel de documento que aparecen de manera común en la salida de los sistemas Estadísticos del Traducción Automática (SMT). Para dos de estos errores, la traducción inconsistente de palabras, así como los desacuerdos en género y número entre palabras, diseñamos técnicas simples pero efectivas como post-procesos para tratarlos y corregirlos. Como estas técnicas se aplican a posteriori, pueden acceder a los documentos enteros tanto del origen como la traducción generada, y así son capaces de hacer un análisis global y mejorar la coherencia y la consistencia de la traducción. Sin embargo, como seguir una estrategia de traducción en dos pasos no es óptima en términos de eficiencia, también nos centramos en introducir la conciencia del contexto durante el propio proceso de generación de la traducción. Para esto, extendemos un sistema SMT orientado a documentos incluyendo información semántica distribucional en forma de word embeddings bilingües y monolingües. En particular, estos embeddings se usan como un Modelo de Lenguaje de Espacio Semántico (SSLM) y como una nueva función característica del sistema. La meta del primero es promover traducciones de palabras que sean semánticamente cercanas a su contexto precedente, mientras que la segunda quiere promover la selección léxica que es más cercana a su contexto para aquellas palabras que tienen diferentes traducciones a lo largo de un documento. En ambos casos, el contexto que se tiene en cuenta va más allá de los límites de una frase u oración. Recientemente, la comunidad MT ha hecho una transición hacia el paradigma neuronal. El paso final de nuestra investigación propone una extensión del proceso de decodificación de un sistema de Traducción Automática Neuronal (NMT), independiente de la arquitectura del modelo de traducción, aplicando la técnica de Shallow Fusion para combinar la información del modelo de traducción neuronal y la información semántica del contexto encerrada en los modelos SSLM estudiados previamente. La motivación de esta modificación está en introducir los beneficios de la información del contexto también en el proceso de decodificación de los sistemas NMT, así como también obtener una validación adicional para las técnicas que se han ido explorando a lo largo de esta tesis. La evaluación automática de nuestras propuestas no refleja variaciones significativas. Esto es un comportamiento esperado ya que la mayoría de las métricas automáticas no se diseñan para ser sensibles al contexto o a la semántica, y además los fenómenos que tratamos son escasos, llevando a pocas modificaciones con respecto a las traducciones de partida. Por otro lado, las evaluaciones manuales demuestran el impacto positivo de nuestras propuestas ya que los evaluadores humanos tienen a preferir las traducciones generadas por nuestros sistemas a nivel de documento. Entonces, los cambios introducidos por nuestros sistemas extendidos son importantes porque están relacionados con la forma en que los humanos perciben la calidad de la traducción de textos largos.
Книги з теми "Machine translations"
The naked machine: Selected poems. Reykjavík: Almenna Bókafélagiđ, 1988.
Знайти повний текст джерелаJohannessen, Matthías. The naked machine: Selected poems. Reykjavík: Almenna Bókafélagiđ, 1988.
Знайти повний текст джерелаJohannessen, Matthías. The naked machine: Selected poems of Matthías Johannessen. Reykjav ́k: Almenna Bókafélagid, 1988.
Знайти повний текст джерелаChrista, Hauenschild, and Heizmann Susanne 1963-, eds. Machine translation and translation theory. Berlin: Mouton de Gruyter, 1997.
Знайти повний текст джерелаThe Ghost in the Shell 2: Man-Machine Interface. New York, USA: Kodansha America, Incorporated, 2016.
Знайти повний текст джерелаSu, Jinsong, and Rico Sennrich, eds. Machine Translation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7512-6.
Повний текст джерелаShi, Xiaodong, and Yidong Chen, eds. Machine Translation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45701-6.
Повний текст джерелаChen, Jiajun, and Jiajun Zhang, eds. Machine Translation. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3083-4.
Повний текст джерелаYang, Muyun, and Shujie Liu, eds. Machine Translation. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3635-4.
Повний текст джерелаHuang, Shujian, and Kevin Knight, eds. Machine Translation. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1721-1.
Повний текст джерелаЧастини книг з теми "Machine translations"
Daems, Joke, and Lieve Macken. "Post-Editing Human Translations and Revising Machine Translations." In Translation Revision and Post-Editing, 50–70. London ; New York : Rutledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9781003096962-5.
Повний текст джерелаKumar, Ritesh. "Making Machine Translations Polite: The Problematic Speech Acts." In Information Systems for Indian Languages, 185–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_29.
Повний текст джерелаGreiner-Petter, André. "From LaTeX to Computer Algebra System." In Making Presentation Math Computable, 95–112. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40473-4_4.
Повний текст джерелаSun, Juan, Zhi Lu, Isabel Lacruz, Lijun Ma, Lin Fan, Xiuhua Huang, and Bo Zhou. "Chapter 4. An eye-tracking study of productivity and effort in Chinese-to-English translation and post-editing." In American Translators Association Scholarly Monograph Series, 57–82. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/ata.xx.04sun.
Повний текст джерелаCarter, 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, 49–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30353-1_5.
Повний текст джерелаSong, Yuting, Biligsaikhan Batjargal, and Akira Maeda. "A Preliminary Attempt to Evaluate Machine Translations of Ukiyo-e Metadata Records." In Digital Libraries at Times of Massive Societal Transition, 262–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64452-9_24.
Повний текст джерелаWeber, Jutta. "Black-Boxing Organisms, Exploiting the Unpredictable: Control Paradigms in Human–Machine Translations." In Science in the Context of Application, 409–29. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9051-5_24.
Повний текст джерелаDomingo, Miguel, and Francisco Casacuberta. "A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization." In Pattern Recognition. ICPR International Workshops and Challenges, 326–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68787-8_24.
Повний текст джерелаEl-Haj, Mahmoud, Paul Rayson, and David Hall. "Language Independent Evaluation of Translation Style and Consistency: Comparing Human and Machine Translations of Camus’ Novel “The Stranger”." In Text, Speech and Dialogue, 116–24. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10816-2_15.
Повний текст джерелаChiang, 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.
Повний текст джерелаТези доповідей конференцій з теми "Machine translations"
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.
Повний текст джерелаZhang, Wu, Tung Yeung Lam, and Mee Yee Chan. "Using Translation Memory to Improve Neural Machine Translations." In ICDLT 2022: 2022 6th International Conference on Deep Learning Technologies. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3556677.3556691.
Повний текст джерелаMeng, 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.
Повний текст джерелаMarie, Benjamin, and Atsushi Fujita. "Unsupervised Extraction of Partial Translations for Neural Machine Translation." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1384.
Повний текст джерелаInkova, O., and V. Nuriev. "Divergent translation of connectives in human and machine translations." In Computational Linguistics and Intellectual Technologies. Russian State University for the Humanities, 2021. http://dx.doi.org/10.28995/2075-7182-2021-20-339-348.
Повний текст джерелаChen, Shizhe, Qin Jin, and Jianlong Fu. "From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/685.
Повний текст джерелаEriguchi, Akiko, Shufang Xie, Tao Qin, and Hany Hassan. "Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-main.44.
Повний текст джерелаNavlea, Mirabela. "IMPACT OF ONLINE MACHINE TRANSLATION SYSTEMS ON LIFELONG LEARNERS." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-082.
Повний текст джерелаBizzoni, Yuri, Tom S. Juzek, Cristina España-Bonet, Koel Dutta Chowdhury, Josef van Genabith, and Elke Teich. "How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech." In Proceedings of the 17th International Conference on Spoken Language Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.iwslt-1.34.
Повний текст джерелаSun, Liqun, and Zhi Quan Zhou. "Metamorphic Testing for Machine Translations: MT4MT." In 2018 25th Australasian Software Engineering Conference (ASWEC). IEEE, 2018. http://dx.doi.org/10.1109/aswec.2018.00021.
Повний текст джерелаЗвіти організацій з теми "Machine translations"
Walrath, James D. Evidence for Increased Discriminability in Judging the Acceptability of Machine Translations: The Case for Magnitude Estimation. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada499858.
Повний текст джерелаMorgan, John J. Project-specific Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada554967.
Повний текст джерелаHobbs, 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.
Повний текст джерелаDorr, Bonnie J. Principle-Based Parsing for Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada199183.
Повний текст джерелаChurch, 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.
Повний текст джерелаLee, Young-Suk. Morphological Analysis for Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada460276.
Повний текст джерелаLopez, Adam. A Survey of Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada466330.
Повний текст джерелаTurian, 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.
Повний текст джерелаRusso-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.
Повний текст джерелаGermann, 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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