Neural Machine Translation Based on Back-Translation for Multilingual Translation Evaluation Task

This paper presents the systems developed by Beijing Jiaotong University for the CCMT 2020 multilingual translation evaluation task. For this translation task, we need to build a Japanese-English translation system based on only Japanese-Chinese and English-Chinese data. Our method mainly relies on synthetic data generated by back translation. We implemented three different architectures, namely Transformer-big, Transformer-base and Dynamic-Conv. We also implemented multi-model ensemble technique to further boost the final result. Experiments show that our machine translation system achieved high accuracy without relying on any bilingual training data.