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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Fujita, Atsushi; Marie, Benjamin. - : Underline Science Inc., 2021 BASE Show details 2 Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Fujita, Atsushi; Marie, Benjamin. - : Underline Science Inc., 2021 BASE Show details 3 Softmax Tempering for Training Neural Machine Translation Models ... Dabre, Raj; Fujita, Atsushi. - : arXiv, 2020 BASE Show details 4 Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation ... Imankulova, Aizhan; Dabre, Raj; Fujita, Atsushi; Imamura, Kenji. - : arXiv, 2019 Abstract: This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking. Although there are many solutions for low-resource scenarios, such as multilingual NMT and back-translation, we have empirically confirmed their limited success when restricted to in-domain data. We therefore propose to exploit out-of-domain data through transfer learning, by using it to first train a multilingual NMT model followed by multistage fine-tuning on in-domain parallel and back-translated pseudo-parallel data. Our approach, which combines domain adaptation, multilingualism, and back-translation, helps improve the translation quality by more than 3.7 BLEU points, over a strong baseline, for this extremely low-resource scenario. ... : Accepted at the 17th Machine Translation Summit ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1907.03060 https://arxiv.org/abs/1907.03060 BASE Hide details

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