DiDi's Machine Translation System for WMT2020
This is an incremental improvement for machine translation systems in a specific competition setting.
The paper describes DiDi AI Labs' submission to the WMT2020 Chinese->English news translation task, achieving a BLEU score of 36.6 by integrating techniques like data filtering and model ensembling on a Transformer baseline.
This paper describes DiDi AI Labs' submission to the WMT2020 news translation shared task. We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model, and integrate several techniques for model enhancement, including data filtering, data selection, back-translation, fine-tuning, model ensembling, and re-ranking. As a result, our submission achieves a BLEU score of $36.6$ in Chinese->English.