Abstract
This paper describes the POSTECH’s submission to the WMT 2018 shared task on Automatic Post-Editing (APE). We propose a new neural end-to-end post-editing model based on the transformer network. We modified the encoder-decoder attention to reflect the relation between the machine translation output, the source and the post-edited translation in APE problem. Experiments on WMT17 English-German APE data set show an improvement in both TER and BLEU score over the best result of WMT17 APE shared task. Our primary submission achieves -4.52 TER and +6.81 BLEU score on PBSMT task and -0.13 TER and +0.40 BLEU score for NMT task compare to the baseline.- Anthology ID:
- W18-6470
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 840–845
- Language:
- URL:
- https://aclanthology.org/W18-6470
- DOI:
- 10.18653/v1/W18-6470
- Cite (ACL):
- Jaehun Shin and Jong-Hyeok Lee. 2018. Multi-encoder Transformer Network for Automatic Post-Editing. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 840–845, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Multi-encoder Transformer Network for Automatic Post-Editing (Shin & Lee, WMT 2018)
- PDF:
- https://preview.aclanthology.org/author-url/W18-6470.pdf