Abstract
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.- Anthology ID:
- 2021.alta-1.18
- Volume:
- Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2021
- Address:
- Online
- Editors:
- Afshin Rahimi, William Lane, Guido Zuccon
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 169–173
- Language:
- URL:
- https://aclanthology.org/2021.alta-1.18
- DOI:
- Cite (ACL):
- Thanh Vu and Dai Quoc Nguyen. 2021. Automatic Post-Editing for Vietnamese. In Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association, pages 169–173, Online. Australasian Language Technology Association.
- Cite (Informal):
- Automatic Post-Editing for Vietnamese (Vu & Nguyen, ALTA 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.alta-1.18.pdf
- Code
- tienthanhdhcn/VnAPE