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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; Chaudhary, Vishrav; Goyal, Naman; Guzmán, Francisco; Fung, Pascale; Koehn, Philipp; Diab, Mona. - : arXiv, 2021 Abstract: The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines. ... : ACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2105.15071 https://dx.doi.org/10.48550/arxiv.2105.15071 BASE Hide details 6 An exploratory study on multilingual quality estimation Renduchintala, Adithya; Fomicheva, Marina; Blain, Frederic... In: 366 ; 377 (2020) BASE Show details

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