HW-TSC 2023 Submission for the Quality Estimation Shared Task
Yuang Li, Chang Su, Ming Zhu, Mengyao Piao, Xinglin Lyu, Min Zhang, Hao Yang · 2023
Quality estimation (QE) is an essential technique to assess machine translation quality without reference translations.In this paper, we focus on Huawei Translation Services Center's (HW-TSC's) submission to the sentence-level QE shared task, named Ensemble-CrossQE.Our system uses CrossQE, the same model architecture as our last year's submission, which consists of a multilingual base model and a task-specific downstream layer.The input is the concatenation of the source and the translated sentences.To enhance the performance, we finetuned and ensembled multiple base models such as XLM-R, InfoXLM, RemBERT and CometKiwi.Moreover, we introduce a new corruption-based data augmentation method, which generates deletion, substitution and insertion errors in the original translation and uses a reference-based QE model to obtain pseudo scores.Results show that our system achieves impressive performance on sentence-level QE test sets and ranked the first place for three language pairs: English-Hindi, English-Tamil and English-Telegu 1 .In addition, we participated in the error span detection task.The submitted model outperforms the baseline on Chinese-English and Hebrew-English language pairs.