NICT’s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task
Raj Dabre, Eiichiro Sumita · 2019
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.