2019 ACL ACL 2019

System Description: The Submission of FOKUS to the WMT 19 Robustness Task

Abstract

AbstractThis paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.

🧭 Keyword Pioneer — biomedical translation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Natural Language Processing, Speech & Audio
🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Natural Language Processing

Authors