Abstract
Machine translation is one of the most popular areas in natural language processing. WMT is a conference to assess the level of machine translation capabilities of organizations around the world, which is the evaluation activity we participated in. In this review we participated in a two-way translation track from Russian to English and English to Russian. We used official training data, 38 million parallel corpora, and 10 million monolingual corpora. The overall framework we use is the Transformer neural machine translation model, supplemented by data filtering, post-processing, reordering and other related processing methods. The BLEU value of our final translation result from Russian to English is 38.7, ranking 5th, while from English to Russian is 27.8, ranking 10th.- Anthology ID:
- W19-5349
- 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:
- 434–439
- Language:
- URL:
- https://aclanthology.org/W19-5349
- DOI:
- 10.18653/v1/W19-5349
- Cite (ACL):
- Doron Yu. 2019. The En-Ru Two-way Integrated Machine Translation System Based on Transformer. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 434–439, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The En-Ru Two-way Integrated Machine Translation System Based on Transformer (Yu, WMT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W19-5349.pdf