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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 – 6 of 6 1 Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation ... Renduchintala, Adithya; Diaz, Denise; Heafield, Kenneth. - : arXiv, 2021 BASE Show details 2 Investigating Failures of Automatic Translation in the Case of Unambiguous Gender ... Renduchintala, Adithya; Williams, Adina. - : arXiv, 2021 BASE Show details 3 Gender bias amplification during Speed-Quality optimization in 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; Diab, Mona; Diaz, Denise. - : Underline Science Inc., 2021 BASE Show details 4 XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ... El-Kishky, Ahmed; Renduchintala, Adithya; Cross, James. - : arXiv, 2021 BASE Show details 5 Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ... Ko, Wei-Jen; El-Kishky, Ahmed; Renduchintala, Adithya. - : arXiv, 2021 BASE Show details 6 An exploratory study on multilingual quality estimation Renduchintala, Adithya; Fomicheva, Marina; Blain, Frederic; Guzman, Francisco; Chaudhary, Vishrav; Sun, Shuo; Specia, Lucia; El-Kishky, Ahmed In: 366 ; 377 (2020) Abstract: This is an accepted manuscript of an article published by ACL, available online at: https://www.aclweb.org/anthology/2020.aacl-main.39 The accepted version of the publication may differ from the final published version. ; Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages. An obvious disadvantage of this approach is the need for labelled data for each given language pair. We challenge this assumption by exploring different approaches to multilingual Quality Estimation (QE), including using scores from translation models. We show that these outperform singlelanguage models, particularly in less balanced quality label distributions and low-resource settings. In the extreme case of zero-shot QE, we show that it is possible to accurately predict quality for any given new language from models trained on other languages. Our findings indicate that state-of-the-art neural QE models based on powerful pre-trained representations generalise well across languages, making them more applicable in real-world settings. Keyword: machine translation; multilingual; multitask learning; quality estimation; zero-shot learning URL: http://hdl.handle.net/2436/623698 BASE Hide details

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