Abstract
This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re-implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT post-editing sub-task establishing the new state-of-the-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.- Anthology ID:
- W18-6467
- 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:
- 822–826
- Language:
- URL:
- https://aclanthology.org/W18-6467
- DOI:
- 10.18653/v1/W18-6467
- Cite (ACL):
- Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2018. MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 822–826, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing (Junczys-Dowmunt & Grundkiewicz, WMT 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6467.pdf
- Data
- eSCAPE