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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 Abstract: Read paper: https://doi.org/10.1162/tacla00341 Abstract: Neural machine translation (NMT) systems are usually trained on clean parallel data. They can perform very well for translating clean in-domain texts. However, as demonstrated by previous work, the translation quality significantly worsens when translating noisy texts, such as user-generated texts (UGT) from online social media. Given the lack of parallel data of UGT that can be used to train or adapt NMT systems, we synthesize parallel data of UGT, exploiting monolingual data of UGT through cross-lingual language model pre-training and zero-shot NMT systems. This paper presents two different but complementary approaches: one alters given clean parallel data into UGT-like parallel data whereas the other generates translations from monolingual data of UGT. On the MTNT translation tasks, we show that our synthesized parallel data can lead to better NMT systems for UGT while making them more robust in translating texts from various domains and styles. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/kf6w-md76 https://underline.io/lecture/26067-synthesizing-parallel-data-of-user-generated-texts-with-zero-shot-neural-machine-translation BASE Hide 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. - : arXiv, 2019 BASE Show details

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