Results 41 to 50 of about 47,700 (259)
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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Priming Neural Machine Translation
Proceedings of the Fifth Conference on Machine Translation, 2020 Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT).
Pham, Minh Quang +4 more
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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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Neural Machine Translation with Contrastive Translation Memories
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Différents des travaux antérieurs qui utilisent des mémoires de traduction (TM) similaires mais redondantes, nous proposons un nouveau NMT à récupération augmentée pour modéliser des mémoires de traduction récupérées de manière contrastée qui sont globalement similaires à la phrase source tout en étant individuellement contrastives les unes par rapport
Xiuzhen Cheng +4 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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Tensor2Tensor for Neural Machine Translation
CoRR, 2018 Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.
Ashish Vaswani +12 more
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Deep architectures for Neural Machine Translation [PDF]
Proceedings of the Second Conference on Machine Translation, 2017 WMT 2017 research ...
Antonio Valerio Miceli Barone +4 more
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