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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 – 8 of 8 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 Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ... Briakou, Eleftheria; Agrawal, Sweta; Tetreault, Joel. - : arXiv, 2021 BASE Show details 3 Can Multilinguality benefit Non-autoregressive Machine Translation? ... Agrawal, Sweta; Kreutzer, Julia; Cherry, Colin. - : arXiv, 2021 BASE Show details 4 Assessing Reference-Free Peer Evaluation for Machine Translation ... Agrawal, Sweta; Foster, George; Freitag, Markus. - : arXiv, 2021 BASE Show details 5 Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Joel; Agrawal, Sweta. - : Underline Science Inc., 2021 BASE Show details 6 Assessing Reference-Free Peer Evaluation for Machine Translation ... NAACL 2021 2021; Agrawal, Sweta. - : Underline Science Inc., 2021 Abstract: Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.91/ Abstract: Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state-of-the-art results when used as a reference-free metric. We experiment with various modifications to this model and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities. ... URL: https://dx.doi.org/10.48448/4rnv-ed14 https://underline.io/lecture/19893-assessing-reference-free-peer-evaluation-for-machine-translation BASE Hide details 7 A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ... 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; Agrawal, Sweta; Carpuat, Marine. - : Underline Science Inc., 2021 BASE Show details 8 Controlling Text Complexity in Neural Machine Translation ... Agrawal, Sweta; Carpuat, Marine. - : arXiv, 2019 BASE Show details

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