Abstract
In this paper we describe our neural machine translation (NMT) systems for Japanese↔English translation which we submitted to the translation robustness task. We focused on leveraging transfer learning via fine tuning to improve translation quality. We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et. al., 2017) to improve translation quality for Japanese↔English. We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios. However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest.- Anthology ID:
- W19-5362
- 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:
- 533–536
- Language:
- URL:
- https://aclanthology.org/W19-5362
- DOI:
- 10.18653/v1/W19-5362
- Cite (ACL):
- Raj Dabre and Eiichiro Sumita. 2019. NICT’s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 533–536, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- NICT’s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task (Dabre & Sumita, WMT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W19-5362.pdf
- Data
- MTNT