Results 31 to 40 of about 3,692,894 (287)
Neural Name Translation Improves Neural Machine Translation
CoRR, 2016 In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered unks to learn the correspondence between source and target rare words. However, testing words unseen in the training corpus
Xiaoqing Li +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 (
Xing Wang 0007 +5 more
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Machine translation using natural language processing [PDF]
MATEC Web of Conferences, 2019 Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation.
Rishita Middi Venkata Sai +2 more
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JITeCS (Journal of Information Technology and Computer Science), 2022 People relatively use machine translation to learn any textual knowledge beyond their native language. There is already robust machine translation such as Google translate.
I Gede Bintang Arya Budaya +2 more
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Variational Neural Machine Translation [PDF]
Proceedings of the 2016 Conference on Empirical Methods in Natural
Language Processing, 2016 Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-
Biao Zhang 0002 +4 more
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Generative Neural Machine Translation
CoRR, 2018 We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence.
Harshil Shah, David Barber
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Low-Resource Neural Machine Translation: A Systematic Literature Review
IEEE Access, 2023 In this study, a systematic literature review was conducted to examine the significant works in the literature on low-resource neural machine translation. Within the scope of the study, three research questions were identified to examine the low-resource
Bilge Kagan Yazar +2 more
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On Compositionality in Neural Machine Translation
CoRR, 2019 We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond the observed length in training data and (2) Systematicity - the ability of the model to systematically recombine known parts and rules.
Vikas Raunak +2 more
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Compositional Source Word Representations for Neural Machine Translation [PDF]
, 2018 The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability.
duygu Ataman +5 more
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Evaluating Neural Machine Translation Using Error Analysis In English -Arabic Texts [PDF]
مجلة کلية الآداب. جامعة أسوان, 2019 The aim of this study was to evaluate the output of Neural Machine Translation of translating texts from English into Arabic using error analysis. Google Translate was taken as an example as the leading neural machine translations.
فهد بن سعد السهلي
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