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Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output (2021.emnlp-main)

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Challenge: Post-editing (PE) machine translation (MT) output can save time and reduce errors.
Approach: They propose to use automatic word-level quality estimation to predict correctness of MT output to flag problematic output.
Outcome: The proposed model is not good enough to support human translations, but is based on a visualization reflecting uncertainty of the model.

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