Results 21 to 30 of about 329,381 (313)
Introducing linguistic knowledge into statistical machine translation. [PDF]
, 2007 Aquesta tesi està dedicada a l'estudi de la utilització de informació morfosintàctica en el marc dels sistemes de traducció estocàstica, amb l'objectiu de millorar-ne la qualitat a través de la incorporació de informació lingüística més enllà del nivell simbòlic superficial de les paraules.<br/><br/><br/><br/>El sistema de ...
Adrià Gispert Ramis
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On Statistical Machine Translation and Translation Theory [PDF]
Proceedings of the Second Workshop on Discourse in Machine Translation, 2015 The translation process in statistical machine translation (SMT) is shaped by technical constraints and engineering considerations. SMT explicitly models translation as search for a target-language equivalent of the input text. This perspective on translation had wide currency in mid-20th century translation studies, but has since been superseded by ...
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Topics in statistical machine translation [PDF]
Tutorial Abstracts of ACL-IJCNLP 2009 on - ACL-IJCNLP '09, 2009 In the past, we presented tutorials called "Introduction to Statistical Machine Translation", aimed at people who know little or nothing about the field and want to get acquainted with the basic concepts. This tutorial, by contrast, goes more deeply into selected topics of intense current interest. We aim at two types of participants: 1.
Kevin Knight, Philipp Koehn
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The Geometry of Statistical Machine Translation [PDF]
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015 Most modern statistical machine translation systems are based on linear statistical models. One extremely effective method for estimating the model parameters is minimum error rate training (MERT), which is an efficient form of line optimisation adapted to the highly nonlinear objective functions used in machine translation.
Aurelien Waite, Bill Byrne
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Cadernos de Tradução, 2020 The essay provides a critical assessment of the performance of two distinct machine translation systems, Systran and Google Translate. First, a brief overview of both rule-based and statistical machine translation systems is provided followed by a ...
Brita Banitz
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MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation
Journal of Intelligent Systems, 2019 Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT).
Mahata Sainik Kumar +2 more
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Neural Machine Translation Advised by Statistical Machine Translation
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016b; 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to conventional Statistical Machine Translation (
Wang, Xing +5 more
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Taking Statistical Machine Translation to the Student Translator [PDF]
, 2012 Despite the growth of statistical machine translation (SMT) research and development in recent years, it remains somewhat out of reach for the translation community where programming expertise and knowledge of statistics tend not to be commonplace. While the concept of SMT is relatively straightforward, its implementation in functioning systems remains
Doherty, Stephen +2 more
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Studia Rossica Posnaniensia, 2019 Machine translation (MT) is a relatively new field of science. MT systems are evolving in certain directions. The article discusses the possibilities and the future of systems currently offered to public by the biggest technological companies focusing on
Jakub Olas
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Treatment of Markup in Statistical Machine Translation [PDF]
Proceedings of the Third Workshop on Discourse in Machine Translation, 2017 We present work on handling XML markup in Statistical Machine Translation (SMT). The methods we propose can be used to effectively preserve markup (for instance inline formatting or structure) and to place markup correctly in a machine-translated segment.
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