Satoshi Tohda


A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size
Masato Neishi | Jin Sakuma | Satoshi Tohda | Shonosuke Ishiwatari | Naoki Yoshinaga | Masashi Toyoda
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

In this paper, we describe the team UT-IIS’s system and results for the WAT 2017 translation tasks. We further investigated several tricks including a novel technique for initializing embedding layers using only the parallel corpus, which increased the BLEU score by 1.28, found a practical large batch size of 256, and gained insights regarding hyperparameter settings. Ultimately, our system obtained a better result than the state-of-the-art system of WAT 2016. Our code is available on