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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 – 10 of 10 1 Knowledge Distillation for Quality Estimation ... Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : arXiv, 2021 BASE Show details 2 Continual Quality Estimation with Online Bayesian Meta-Learning ... 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; Fomicheva, Marina; Obamuyide, Abiola. - : Underline Science Inc., 2021 BASE Show details 3 Knowledge Distillation for Quality Estimation ... 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; Aletras, Nikolaos; Alva-Manchego, Fernando; Blain, Frédéric; Fomicheva, Marina; Gajbhiye, Amit; Obamuyide, Abiola; Specia, Lucia. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.452 Abstract: Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models lead to impressive results. However, the inference time, disk and memory requirements of such models do not allow for wide usage in the real world. Models trained on distilled pre-trained representations remain prohibitively large for many usage scenarios. We instead propose to directly transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. We show that this approach, in combination with data augmentation, leads to light-weight QE models that perform competitively with distilled pre-trained representations with 8x fewer parameters. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/26543-knowledge-distillation-for-quality-estimation https://dx.doi.org/10.48448/cc9y-3821 BASE Hide details 4 Knowledge distillation for quality estimation Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021 BASE Show details 5 deepQuest-py: large and distilled models for quality estimation Alva-Manchego, Fernando; Obamuyide, Abiola; Gajbhiye, Amit. - : Association for Computational Linguistics, 2021 BASE Show details 6 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 7 Knowledge distillation for quality estimation Specia, Lucia; Aletras, Nikolaos; Gajbhiye, Amit... In: 5091 ; 5099 (2021) BASE Show details 8 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 9 The Summa Platform Prototype ... Liepins, Renars; Germann, Ulrich; Barzdins, Guntis. - : Zenodo, 2017 BASE Show details 10 The SUMMA Platform Prototype Riedel, Sebastian; Garner, Phil; Narayan, Shashi... In: http://infoscience.epfl.ch/record/233575 (2017) BASE Show details

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