In this paper we describe the ADAPT Centre’s (Team ID: adapt-dcu) submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) translation task. We only considered translating from Japanese to English for this task and secured the third position in the competition as per the rankings of the MT systems based on the human evaluation scores. The machine translation (MT) systems that we built for this task are state-of-the-art Trans- former models. In order to improve the translation quality of our neural MT (NMT) systems, we made use of both in-domain and out-of- domain data for training. We applied various data augmentation techniques for fine-tuning the model parameters. This paper outlines the experiments we carried out for this task and reports the MT systems’ performance on the evaluation test set.