Results 11 to 20 of about 5,276,450 (332)
Zero-shot Image-to-Image Translation [PDF]
International Conference on Computer Graphics and Interactive Techniques, 2023 Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse, high-quality images. However, directly applying these models for real image editing remains challenging for two reasons. First, it is hard for users to
Gaurav Parmar +5 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
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
Multilingual Denoising Pre-training for Neural Machine Translation [PDF]
Transactions of the Association for Computational Linguistics, 2020 This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
Yinhan Liu +7 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
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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
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
Large Language Models Are State-of-the-Art Evaluators of Translation Quality [PDF]
European Association for Machine Translation Conferences/Workshops, 2023 We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without. In our evaluation, we focus on zero-shot prompting, comparing four prompt variants in two modes, based on the ...
Tom Kocmi, C. Federmann
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