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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 BASE Show 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; Xu, Weijia. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.330 Abstract: We introduce a new approach for the task of Controllable Text Simplification, where systems rewrite a complex English sentence so that it can be understood by readers at different grade levels in the US K-12 system. It uses a non-autoregressive model to iteratively edit an input sequence and incorporates lexical complexity information seamlessly into the refinement process to generate simplifications that better match the desired output complexity than strong autoregressive baselines. The analysis shows that our model's local edit operations are combined to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/x46d-1q96 https://underline.io/lecture/26421-a-non-autoregressive-edit-based-approach-to-controllable-text-simplification BASE Hide details 8 Controlling Text Complexity in Neural Machine Translation ... Agrawal, Sweta; Carpuat, Marine. - : arXiv, 2019 BASE Show details

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