Adaptation of back-translation to automatic post-editing for synthetic data generation
- Title
- Adaptation of back-translation to automatic post-editing for synthetic data generation
- Authors
- Lee, W.; Lee, J.-H.; Shin, J.; Jung, B.
- Date Issued
- 2021-04
- Publisher
- Association for Computational Linguistics (ACL)
- Abstract
- Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system. Although data-driven approaches have become prevalent also in the APE task as in many other NLP tasks, there has been a lack of qualified training data due to the high cost of manual construction. eSCAPE, a synthetic APE corpus, has been widely used to alleviate the data scarcity, but it might not address genuine APE corpora's characteristic that the post-edited sentence should be a minimally edited revision of the given MT output. Therefore, we propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of the existing synthetic APE training dataset1. Experimental results on the WMT English-German APE benchmarks demonstrate that our enlarged datasets are effective in improving APE performance. ? 2021 Association for Computational Linguistics
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/109921
- ISSN
- 0000-0000
- Article Type
- Conference
- Citation
- 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, page. 3685 - 3691, 2021-04
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