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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 – 11 of 11 1 Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets Caswell, Isaac; Kreutzer, Julia; Wang, Lisa... In: https://hal.inria.fr/hal-03177623 ; 2021 (2021) BASE Show details 2 MasakhaNER: Named entity recognition for African languages Adelani, David,; Abbott, Jade; Neubig, Graham... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021) BASE Show details 3 Modelling Latent Translations for Cross-Lingual Transfer ... Ponti, Edoardo Maria; Kreutzer, Julia; Vulić, Ivan. - : arXiv, 2021 BASE Show details 4 Can Multilinguality benefit Non-autoregressive Machine Translation? ... Agrawal, Sweta; Kreutzer, Julia; Cherry, Colin. - : arXiv, 2021 BASE Show details 5 Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets ... Kreutzer, Julia; Caswell, Isaac; Wang, Lisa. - : arXiv, 2021 BASE Show details 6 Evaluating Multiway Multilingual NMT in the Turkic Languages ... Mirzakhalov, Jamshidbek; Babu, Anoop; Kunafin, Aigiz; Wahab, Ahsan; Moydinboyev, Behzod; Ivanova, Sardana; Uzokova, Mokhiyakhon; Pulatova, Shaxnoza; Ataman, Duygu; Kreutzer, Julia; Tyers, Francis; Firat, Orhan; Licato, John; Chellappan, Sriram. - : arXiv, 2021 Abstract: Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our ... : 9 pages, 3 figures, 7 tables. To be presented at WMT 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2109.06262 https://arxiv.org/abs/2109.06262 BASE Hide details 7 The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ahia, Orevaoghene; Hooker, Sara. - : Underline Science Inc., 2021 BASE Show details 8 Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications ... Kreutzer, Julia. - : Heidelberg University Library, 2020 BASE Show details 9 Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara ... Tapo, Allahsera Auguste; Coulibaly, Bakary; Diarra, Sébastien. - : arXiv, 2020 BASE Show details 10 Participatory Research for Low-resourced Machine Translation:A Case Study in African Languages Nekoto, Wilhelmina; Marivate, Vukosi; Matsila, Tshinondiwa. - 2020 BASE Show details 11 Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications Kreutzer, Julia. - 2020 BASE Show details

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