Hanming Wu


BJTU-Toshiba’s Submission to WMT22 Quality Estimation Shared Task
Hui Huang | Hui Di | Chunyou Li | Hanming Wu | Kazushige Ouchi | Yufeng Chen | Jian Liu | Jinan Xu
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the BJTU-Toshiba joint submission for WMT 2022 quality estimation shared task. We only participate in Task 1 (quality prediction) of the shared task, focusing on the sentence-level MQM prediction. The techniques we experimented with include the integration of monolingual language models and the pre-finetuning of pre-trained representations. We tried two styles of pre-finetuning, namely Translation Language Modeling and Replaced Token Detection. We demonstrate the competitiveness of our system compared to the widely adopted XLM-RoBERTa baseline. Our system is also the top-ranking system on the Sentence-level MQM Prediction for the English-German language pairs.