Results 41 to 50 of about 240,752 (266)
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
, 2017 Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve.
Boyd-Graber, Jordan +2 more
core +1 more source
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
openaire +2 more sources
Binarized Neural Machine Translation
, 2023 The rapid scaling of language models is motivating research using low-bitwidth quantization. In this work, we propose a novel binarization technique for Transformers applied to machine translation (BMT), the first of its kind. We identify and address the problem of inflated dot-product variance when using one-bit weights and activations.
Zhang, Yichi +6 more
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DESIGN OF TRANSLATION AMBIGUITY ELIMINATION METHOD BASED ON RECURRENT NEURAL NETWORKS [PDF]
Acta Informatica MalaysiaThe ambiguity of language inevitably leads to the ambiguity of translation, and how to deal with translation ambiguity has become a persistent focus of attention for both human translation and machine translation.
Jianzhou Cui
doaj +1 more source
Learning to Parse and Translate Improves Neural Machine Translation
, 2017 There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side.
Cho, Kyunghyun +2 more
core +1 more source
Neural Name Translation Improves Neural Machine Translation
, 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
Li, Xiaoqing +2 more
openaire +2 more sources
Translating Phrases in Neural Machine Translation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Accepted by EMNLP ...
Wang, Xing +3 more
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Cogent Arts & Humanities, 2023 This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and
Mohammed Ali Mohsen +2 more
doaj +1 more source
Syntax-Informed Interactive Neural Machine Translation [PDF]
2020 International Joint Conference on Neural Networks (IJCNN), 2020 In interactive machine translation (MT), human translators correct errors in automatic translations in collaboration with the MT systems, and this is an effective way to improve productivity gain in translation. Phrase-based statistical MT (PB-SMT) has been the mainstream approach to MT for the past 30 years, both in academia and industry.
Gupta, Kamal Kumar +4 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
doaj +1 more source