Abstract
We describe LMU Munich’s machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation. We specifically participated in the document-level MT track. The system used as a primary submission is a context-aware Transformer capable of both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation. We train this model by fine-tuning a big Transformer baseline. Our experimental results show that document-level context provides for large improvements in translation quality, and adding a rich representation of the previous sentence provides a small additional gain.- Anthology ID:
- W19-5345
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 400–406
- Language:
- URL:
- https://aclanthology.org/W19-5345
- DOI:
- 10.18653/v1/W19-5345
- Cite (ACL):
- Dario Stojanovski and Alexander Fraser. 2019. Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 400–406, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19 (Stojanovski & Fraser, WMT 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5345.pdf