Abstract
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.- Anthology ID:
- D18-1341
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3048–3053
- Language:
- URL:
- https://aclanthology.org/D18-1341
- DOI:
- 10.18653/v1/D18-1341
- Cite (ACL):
- Thuy-Trang Vu and Gholamreza Haffari. 2018. Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3048–3053, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (Vu & Haffari, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D18-1341.pdf
- Data
- WMT 2016