@inproceedings{shan-etal-2024-hw,
title = "{HW}-{TSC} 2024 Submission for the Quality Estimation Shared Task",
author = "Shan, Weiqiao and
Zhu, Ming and
Li, Yuang and
Piao, Mengyao and
Zhao, Xiaofeng and
Su, Chang and
Zhang, Min and
Yang, Hao and
Jiang, Yanfei",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.39",
pages = "535--540",
abstract = "Quality estimation (QE) is a crucial technique for evaluating the quality of machine translations without the need for reference translations. This paper focuses on Huawei Translation Services Center{'}s (HW-TSC{'}s) submission to the sentence-level QE shared task, named LLMs-enhanced-CrossQE. Our system builds upon the CrossQE architecture from our submission from last year, which consists of a multilingual base model and a task-specific downstream layer. The model input is a concatenation of the source and the translated sentences. To enhance performance, we fine-tuned and ensembled multiple base models, including XLM-R, InfoXLM, RemBERT, and CometKiwi. Specifically, we employed two pseudo-data generation methods: 1) a diverse pseudo-data generation method based on the corruption-based data augmentation technique introduced last year, and 2) a pseudo-data generation method that simulates machine translation errors using large language models (LLMs). Our results demonstrate that the system achieves outstanding performance on sentence-level QE test sets.",
}
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<abstract>Quality estimation (QE) is a crucial technique for evaluating the quality of machine translations without the need for reference translations. This paper focuses on Huawei Translation Services Center’s (HW-TSC’s) submission to the sentence-level QE shared task, named LLMs-enhanced-CrossQE. Our system builds upon the CrossQE architecture from our submission from last year, which consists of a multilingual base model and a task-specific downstream layer. The model input is a concatenation of the source and the translated sentences. To enhance performance, we fine-tuned and ensembled multiple base models, including XLM-R, InfoXLM, RemBERT, and CometKiwi. Specifically, we employed two pseudo-data generation methods: 1) a diverse pseudo-data generation method based on the corruption-based data augmentation technique introduced last year, and 2) a pseudo-data generation method that simulates machine translation errors using large language models (LLMs). Our results demonstrate that the system achieves outstanding performance on sentence-level QE test sets.</abstract>
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%0 Conference Proceedings
%T HW-TSC 2024 Submission for the Quality Estimation Shared Task
%A Shan, Weiqiao
%A Zhu, Ming
%A Li, Yuang
%A Piao, Mengyao
%A Zhao, Xiaofeng
%A Su, Chang
%A Zhang, Min
%A Yang, Hao
%A Jiang, Yanfei
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F shan-etal-2024-hw
%X Quality estimation (QE) is a crucial technique for evaluating the quality of machine translations without the need for reference translations. This paper focuses on Huawei Translation Services Center’s (HW-TSC’s) submission to the sentence-level QE shared task, named LLMs-enhanced-CrossQE. Our system builds upon the CrossQE architecture from our submission from last year, which consists of a multilingual base model and a task-specific downstream layer. The model input is a concatenation of the source and the translated sentences. To enhance performance, we fine-tuned and ensembled multiple base models, including XLM-R, InfoXLM, RemBERT, and CometKiwi. Specifically, we employed two pseudo-data generation methods: 1) a diverse pseudo-data generation method based on the corruption-based data augmentation technique introduced last year, and 2) a pseudo-data generation method that simulates machine translation errors using large language models (LLMs). Our results demonstrate that the system achieves outstanding performance on sentence-level QE test sets.
%U https://aclanthology.org/2024.wmt-1.39
%P 535-540
Markdown (Informal)
[HW-TSC 2024 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2024.wmt-1.39) (Shan et al., WMT 2024)
ACL
- Weiqiao Shan, Ming Zhu, Yuang Li, Mengyao Piao, Xiaofeng Zhao, Chang Su, Min Zhang, Hao Yang, and Yanfei Jiang. 2024. HW-TSC 2024 Submission for the Quality Estimation Shared Task. In Proceedings of the Ninth Conference on Machine Translation, pages 535–540, Miami, Florida, USA. Association for Computational Linguistics.