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            <title>Proceedings of the Eighth Conference on Machine Translation</title>
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    <abstract>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. In addition, we participated in the error span detection task. The submitted model outperforms the baseline on Chinese-English and Hebrew-English language pairs.</abstract>
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%0 Conference Proceedings
%T HW-TSC 2023 Submission for the Quality Estimation Shared Task
%A Li, Yuang
%A Su, Chang
%A Zhu, Ming
%A Piao, Mengyao
%A Lyu, Xinglin
%A Zhang, Min
%A Yang, Hao
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-hw-tsc
%X 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. In addition, we participated in the error span detection task. The submitted model outperforms the baseline on Chinese-English and Hebrew-English language pairs.
%R 10.18653/v1/2023.wmt-1.72
%U https://aclanthology.org/2023.wmt-1.72/
%U https://doi.org/10.18653/v1/2023.wmt-1.72
%P 835-840

Markdown (Informal)

[HW-TSC 2023 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2023.wmt-1.72/) (Li et al., WMT 2023)

• HW-TSC 2023 Submission for the Quality Estimation Shared Task (Li et al., WMT 2023)

ACL

• Yuang Li, Chang Su, Ming Zhu, Mengyao Piao, Xinglin Lyu, Min Zhang, and Hao Yang. 2023. HW-TSC 2023 Submission for the Quality Estimation Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 835–840, Singapore. Association for Computational Linguistics.