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        <title>DUTNLP System for the WMT2023 Discourse-Level Literary Translation</title>
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            <title>Proceedings of the Eighth Conference on Machine Translation</title>
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    <abstract>This paper describes the submission of DUTNLP Lab submission to WMT23 Discourse-Level Literary Translation in the Chinese to English translation direction under unconstrained conditions. Our primary system aims to leverage a large language model with various prompt strategies, which can fully investigate the potential capabilities of large language models for discourse-level neural machine translation. Moreover, we test a widely used discourse-level machine translation model, G-transformer, with different training strategies. In our experimental results, the method with large language models achieves a BLEU score of 28.16, while the fine-tuned method scores 25.26. These findings indicate that selecting appropriate prompt strategies based on large language models can significantly improve translation performance compared to traditional model training methods.</abstract>
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%0 Conference Proceedings
%T DUTNLP System for the WMT2023 Discourse-Level Literary Translation
%A Zhao, Anqi
%A Huang, Kaiyu
%A Yu, Hao
%A Huang, Degen
%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 zhao-etal-2023-dutnlp
%X This paper describes the submission of DUTNLP Lab submission to WMT23 Discourse-Level Literary Translation in the Chinese to English translation direction under unconstrained conditions. Our primary system aims to leverage a large language model with various prompt strategies, which can fully investigate the potential capabilities of large language models for discourse-level neural machine translation. Moreover, we test a widely used discourse-level machine translation model, G-transformer, with different training strategies. In our experimental results, the method with large language models achieves a BLEU score of 28.16, while the fine-tuned method scores 25.26. These findings indicate that selecting appropriate prompt strategies based on large language models can significantly improve translation performance compared to traditional model training methods.
%R 10.18653/v1/2023.wmt-1.31
%U https://aclanthology.org/2023.wmt-1.31
%U https://doi.org/10.18653/v1/2023.wmt-1.31
%P 296-301

Markdown (Informal)

[DUTNLP System for the WMT2023 Discourse-Level Literary Translation](https://aclanthology.org/2023.wmt-1.31) (Zhao et al., WMT 2023)

• DUTNLP System for the WMT2023 Discourse-Level Literary Translation (Zhao et al., WMT 2023)

ACL

• Anqi Zhao, Kaiyu Huang, Hao Yu, and Degen Huang. 2023. DUTNLP System for the WMT2023 Discourse-Level Literary Translation. In Proceedings of the Eighth Conference on Machine Translation, pages 296–301, Singapore. Association for Computational Linguistics.