Abstract
This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese↔English news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese→English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems.- Anthology ID:
- W18-6413
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 410–417
- Language:
- URL:
- https://aclanthology.org/W18-6413
- DOI:
- 10.18653/v1/W18-6413
- Cite (ACL):
- Bojie Hu, Ambyer Han, and Shen Huang. 2018. TencentFmRD Neural Machine Translation for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 410–417, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- TencentFmRD Neural Machine Translation for WMT18 (Hu et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-6413.pdf