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        <title>GTCOM and DLUT’s Neural Machine Translation Systems for WMT23</title>
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    <abstract>This paper presents the submission by Global Tone Communication Co., Ltd. and Dalian Univeristy of Technology for the WMT23 shared general Machine Translation (MT) task at the Conference on Empirical Methods in Natural Language Processing (EMNLP). Our participation spans 8 language pairs, including English-Ukrainian, Ukrainian-English, Czech-Ukrainian, English-Hebrew, Hebrew-English, English-Czech, German-English, and Japanese-English. Our systems are designed without any specific constraints or requirements, allowing us to explore a wider range of possibilities in machine translation. We prioritize backtranslation, utilize multilingual translation models, and employ fine-tuning strategies to enhance performance. Additionally, we propose a novel data generation method that leverages human annotation to generate high-quality training data, resulting in improved system performance. Specifically, we use a combination of human-generated and machine-generated data to fine-tune our models, leading to more accurate translations. The automatic evaluation results show that our system ranks first in terms of BLEU score in Ukrainian-English, Hebrew-English, English-Hebrew, and German-English.</abstract>
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%0 Conference Proceedings
%T GTCOM and DLUT’s Neural Machine Translation Systems for WMT23
%A Zong, 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 zong-2023-gtcom
%X This paper presents the submission by Global Tone Communication Co., Ltd. and Dalian Univeristy of Technology for the WMT23 shared general Machine Translation (MT) task at the Conference on Empirical Methods in Natural Language Processing (EMNLP). Our participation spans 8 language pairs, including English-Ukrainian, Ukrainian-English, Czech-Ukrainian, English-Hebrew, Hebrew-English, English-Czech, German-English, and Japanese-English. Our systems are designed without any specific constraints or requirements, allowing us to explore a wider range of possibilities in machine translation. We prioritize backtranslation, utilize multilingual translation models, and employ fine-tuning strategies to enhance performance. Additionally, we propose a novel data generation method that leverages human annotation to generate high-quality training data, resulting in improved system performance. Specifically, we use a combination of human-generated and machine-generated data to fine-tune our models, leading to more accurate translations. The automatic evaluation results show that our system ranks first in terms of BLEU score in Ukrainian-English, Hebrew-English, English-Hebrew, and German-English.
%R 10.18653/v1/2023.wmt-1.20
%U https://aclanthology.org/2023.wmt-1.20
%U https://doi.org/10.18653/v1/2023.wmt-1.20
%P 192-197

Markdown (Informal)

[GTCOM and DLUT’s Neural Machine Translation Systems for WMT23](https://aclanthology.org/2023.wmt-1.20) (Zong, WMT 2023)

• GTCOM and DLUT’s Neural Machine Translation Systems for WMT23 (Zong, WMT 2023)

ACL

• Hao Zong. 2023. GTCOM and DLUT’s Neural Machine Translation Systems for WMT23. In Proceedings of the Eighth Conference on Machine Translation, pages 192–197, Singapore. Association for Computational Linguistics.