Results 11 to 20 of about 5,388,665 (365)
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation [PDF]
arXiv.org, 2023 Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoroughly investigated.
Amr Hendy +8 more
semanticscholar +1 more source
Image-to-Image Translation with Conditional Adversarial Networks [PDF]
Computer Vision and Pattern Recognition, 2016 We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping.
Phillip Isola +3 more
semanticscholar +1 more source
Health Research Policy and Systems, 2022 The use of collaborative health research approaches, such as integrated knowledge translation (IKT), was challenged during the COVID-19 pandemic due to physical distancing measures and transition to virtual platforms. As IKT trainees (i.e.
Priscilla Medeiros +12 more
doaj +1 more source
Neural Machine Translation of Rare Words with Subword Units [PDF]
Annual Meeting of the Association for Computational Linguistics, 2015 Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary.
Rico Sennrich +2 more
semanticscholar +1 more source
Journal of Biomedical Science, 2022 The novel coronavirus disease (COVID-19) pandemic remains a global public health crisis, presenting a broad range of challenges. To help address some of the main problems, the scientific community has designed vaccines, diagnostic tools and therapeutics ...
Monika Kumari +16 more
doaj +1 more source
Effective Approaches to Attention-based Neural Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation.
Thang Luong +2 more
semanticscholar +1 more source
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
BMC Public Health, 2023 Background The COVID-19 pandemic accelerated the spread of misinformation worldwide. The purpose of this study was to explore perceptions of misinformation and preferred sources of obtaining COVID-19 information from those living in Canada. In particular,
Suvabna Theivendrampillai +6 more
doaj +1 more source
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches [PDF]
SSST@EMNLP, 2014 Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder.
Kyunghyun Cho +3 more
semanticscholar +1 more source
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for ...
Yunjey Choi +5 more
semanticscholar +1 more source