JHU 2019 robustness task system description

Abstract

We describe the JHU submissions to the French-English, Japanese-English, and English-Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR-EN) and a handful of hyperparameters settings (JA↔EN). As expected, our systems performed reasonably.