Abstract
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT’21 Automatic Post-Editing (APE) English-German (En-De) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT’21 test set.- Anthology ID:
- 2021.wmt-1.35
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 315–319
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.35
- DOI:
- Cite (ACL):
- Abhishek Sharma, Prabhakar Gupta, and Anil Nelakanti. 2021. Adapting Neural Machine Translation for Automatic Post-Editing. In Proceedings of the Sixth Conference on Machine Translation, pages 315–319, Online. Association for Computational Linguistics.
- Cite (Informal):
- Adapting Neural Machine Translation for Automatic Post-Editing (Sharma et al., WMT 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.wmt-1.35.pdf
- Data
- WMT 2016, WikiMatrix