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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 – 13 of 13 1 Pushing the right buttons: adversarial evaluation of quality estimation Specia, Lucia; Fomicheva, Marina; Ranasinghe, Tharindu... In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022) BASE Show details 2 Findings of the WMT 2021 shared task on quality estimation Specia, Lucia; Chaudhary, Vishrav; Martins, André... In: 689 ; 730 (2021) BASE Show details 3 deepQuest-py: large and distilled models for quality estimation Gajbhiye, Amit; Specia, Lucia; Obamuyide, Abiola... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021) BASE Show details 4 Knowledge distillation for quality estimation Specia, Lucia; Aletras, Nikolaos; Gajbhiye, Amit... In: 5091 ; 5099 (2021) BASE Show details 5 An exploratory study on multilingual quality estimation Renduchintala, Adithya; Fomicheva, Marina; Blain, Frederic... In: 366 ; 377 (2020) BASE Show details 6 MLQE-PE: A multilingual quality estimation and post-editing dataset Fomicheva, Marina; Sun, Shuo; Fonseca, Erick. - : arXiv, 2020 BASE Show details 7 WMT18 Quality Estimation Shared Task Test Data Specia, Lucia; Logacheva, Varvara; Blain, Frederic. - : University of Sheffield, 2018 BASE Show details 8 WMT18 Quality Estimation Shared Task Training and Development Data Specia, Lucia; Logacheva, Varvara; Blain, Frederic; Fernandez, Ramon; Martins, André. - : University of Sheffield, 2018 Abstract: Training and development data for the WMT18 QE task. Test data will be published as a separate item. This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are: To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system. To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time. To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators. To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs. To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits. Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments. In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant. Keyword: machine learning; machine translation; quality estimation URL: http://hdl.handle.net/11372/LRT-2619 BASE Hide details 9 WMT17 Quality Estimation Shared Test Data Specia, Lucia; Logacheva, Varvara. - : University of Sheffield, 2017 BASE Show details 10 QT21 Data Specia, Lucia. - : QT21 project, 2017 BASE Show details 11 WMT17 Quality Estimation Shared Task Training and Development Data Specia, Lucia; Logacheva, Varvara. - : University of Sheffield, 2017 BASE Show details 12 WMT16 Quality Estimation Shared Task Training and Development Data Specia, Lucia; Logacheva, Varvara; Scarton, Carolina. - : University of Sheffield, 2016 BASE Show details 13 The role of professional experience in post-editing from a quality and productivity perspective Guerberof Arenas, Ana In: Guerberof Arenas, Ana orcid:0000-0001-9820-7074 (2014) The role of professional experience in post-editing from a quality and productivity perspective. In: O'Brien, Sharon orcid:0000-0003-4864-5986 , Winther Balling, Laura, Carl, Michael, Simard, Michel and Specia, Lucia, (eds.) Post-editing of Machien Translation: processes and applications. Cambridge Scholars Publishing, Newcastle upon Tyne, pp. 51-76. ISBN 978-1-4438-5476-4 (2014) BASE Show details

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