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 dataset. Experimental results on the WMT English-German APE benchmarks demonstrate that our enlarged datasets are effective in improving APE performance.- Anthology ID:
- 2021.eacl-main.322
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3685–3691
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.322
- DOI:
- 10.18653/v1/2021.eacl-main.322
- Cite (ACL):
- WonKee Lee, Baikjin Jung, Jaehun Shin, and Jong-Hyeok Lee. 2021. Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3685–3691, Online. Association for Computational Linguistics.
- Cite (Informal):
- Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (Lee et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.eacl-main.322.pdf
- Code
- wonkeelee/ape-backtranslation
- Data
- eSCAPE