Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)
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| Challenge: | Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system. |
| Approach: | They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset. |
| Outcome: | The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset. |
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Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
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| Challenge: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
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| Challenge: | Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing models due to the lack of training data. |
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
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