Results 31 to 40 of about 53,972 (305)

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, Jiajun Zhang 0001, Chengqing Zong   +2 more
openaire   +2 more sources

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, Zhengdong Lu, Zhaopeng Tu, Hang Li 0001, Deyi Xiong, Min Zhang 0005   +5 more
openaire   +2 more sources

The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation

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, Made Windu Antara Kesiman, I Made Gede Sunarya   +2 more
doaj   +1 more source

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, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang 0005   +4 more
openaire   +2 more sources

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, Xu, Jitao, Crego, Josep-Maria, Senellart, Jean, Yvon, François   +4 more
openaire   +3 more sources

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
openaire   +3 more sources

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, Durmus Ozkan Sahin, Erdal Kilic   +2 more
doaj   +1 more source

WordNet gloss translation for under-resourced languages using multilingual neural machine translation

, 2019
In this paper, we translate the glosses in the English WordNet based on the expand approach for improving and generating wordnets with the help of multilingual neural machine translation. Neural Machine Translation (NMT) has recently been applied to many
McCrae, John P., Chakravarthi, Bharathi Raja, Arcan, Mihael   +2 more
core   +2 more sources

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, Vaibhav Kumar, Florian Metze   +2 more
openaire   +2 more sources

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, Samy Bengio, Eugene Brevdo, François Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Lukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, Jakob Uszkoreit   +12 more
openaire   +3 more sources