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1
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Herbig, Nico
;
Krueger, Antonio
;
Shenoy, Raksha
;
van Genabith, Josef
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.799/ Abstract: Compared to fully manual translation, post-editing (PE) machine translation (MT) output can save time and reduce errors. Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output. Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output. Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present word-level QE information to translators. In particular, should word-level QE visualization indicate uncertainty of the QE model or not? In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Machine translation
;
Natural Language Processing
URL:
https://underline.io/lecture/37261-investigating-the-helpfulness-of-word-level-quality-estimation-for-post-editing-machine-translation-output
https://dx.doi.org/10.48448/jz60-cb48
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Multi-modal indicators for estimating perceived cognitive load in post-editing of machine translation [<Journal>]
Herbig, Nico
[Verfasser];
Pal, Santanu
[Verfasser];
Vela, Mihaela
[Verfasser].
DNB Subject Category Language
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