The ADAPT Centre’s Neural MT Systems for the WAT 2020 Document-Level Translation Task

AACL (WAT) 2020  ·  Wandri Jooste, Rejwanul Haque, Andy Way ·

In this paper we describe the ADAPT Centre’s submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) Translation task. We only consider translating from Japanese to English for this task and we use the MarianNMT toolkit to train Transformer models. In order to improve the translation quality, we made use of both in-domain and out-of-domain data for training our Machine Translation (MT) systems, as well as various data augmentation techniques for fine-tuning the model parameters. This paper outlines the experiments we ran to train our systems and report the accuracy achieved through these various experiments.

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