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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. - : arXiv, 2021 BASE Show 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; kreutzer, Julia. - : Underline Science Inc., 2021 Abstract: A ���bigger is better�۝ explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-restricted environments. Compression techniques have taken on renewed importance as a way to bridge the gap. However, evaluation of the trade-offs incurred by popular compression techniques has been centered on high-resource datasets. In this work, we instead consider the impact of compression in a data-limited regime. We introduce the term low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. This is a common setting for NLP for low-resource languages, yet the trade-offs in performance are poorly studied. Our work offers surprising insights into the relationship between capacity and generalization in data-limited regimes for the task of machine translation. Our experiments on magnitude pruning for translations from English into Yoruba, Hausa, Igbo and German show that in ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing; Neural Network URL: https://underline.io/lecture/39484-the-low-resource-double-bind-an-empirical-study-of-pruning-for-low-resource-machine-translation https://dx.doi.org/10.48448/mdq2-6d93 BASE Hide 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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