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1
Towards Explainable Evaluation Metrics for Natural Language Generation ...
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2
Pushing the right buttons: adversarial evaluation of quality estimation
In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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3
Translation Error Detection as Rationale Extraction ...
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4
Knowledge Distillation for Quality Estimation ...
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5
Continual Quality Estimation with Online Bayesian Meta-Learning ...
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6
Knowledge Distillation for Quality Estimation ...
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
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7
Findings of the WMT 2021 Shared Task on Quality Estimation ...
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8
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
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9
Knowledge distillation for quality estimation
Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021
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10
deepQuest-py: large and distilled models for quality estimation
Alva-Manchego, Fernando; Obamuyide, Abiola; Gajbhiye, Amit. - : Association for Computational Linguistics, 2021
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11
Findings of the WMT 2021 shared task on quality estimation
In: 689 ; 730 (2021)
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12
deepQuest-py: large and distilled models for quality estimation
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021)
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13
Backtranslation feedback improves user confidence in MT, not quality
Obregón, Mateo; Fomicheva, Marina; Novák, Michal. - : Association for Computational Linguistics, 2021
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14
Knowledge distillation for quality estimation
In: 5091 ; 5099 (2021)
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15
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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16
Unsupervised quality estimation for neural machine translation
In: 8 ; 539 ; 555 (2020)
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17
An exploratory study on multilingual quality estimation
In: 366 ; 377 (2020)
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18
BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
In: 1010 ; 1017 (2020)
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19
Findings of the WMT 2020 shared task on quality estimation
In: 743 ; 764 (2020)
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20
MLQE-PE: A multilingual quality estimation and post-editing dataset
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