@inproceedings{yang-etal-2024-exploring-traditional,
title = "Exploring the Traditional {NMT} Model and Large Language Model for Chat Translation",
author = "Yang, Jinlong and
Shang, Hengchao and
Wei, Daimeng and
Guo, Jiaxin and
Li, Zongyao and
Wu, Zhanglin and
Rao, Zhiqiang and
Li, Shaojun and
Xie, Yuhao and
Luo, Yuanchang and
Jiawei, Zheng and
Wei, Bin and
Yang, Hao",
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-1.105",
pages = "1031--1037",
abstract = "This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English↔Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring various strategies, including Minimum Bayesian Risk (MBR) decoding and self-training. The results show significant performance improvements in certain directions, with the MBR self-training method achieving the best results. The Large Language Model also discusses the challenges and potential avenues for further research in the field of chat translation.",
}
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<abstract>This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English↔Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring various strategies, including Minimum Bayesian Risk (MBR) decoding and self-training. The results show significant performance improvements in certain directions, with the MBR self-training method achieving the best results. The Large Language Model also discusses the challenges and potential avenues for further research in the field of chat translation.</abstract>
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%0 Conference Proceedings
%T Exploring the Traditional NMT Model and Large Language Model for Chat Translation
%A Yang, Jinlong
%A Shang, Hengchao
%A Wei, Daimeng
%A Guo, Jiaxin
%A Li, Zongyao
%A Wu, Zhanglin
%A Rao, Zhiqiang
%A Li, Shaojun
%A Xie, Yuhao
%A Luo, Yuanchang
%A Jiawei, Zheng
%A Wei, Bin
%A Yang, Hao
%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 yang-etal-2024-exploring-traditional
%X This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English↔Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring various strategies, including Minimum Bayesian Risk (MBR) decoding and self-training. The results show significant performance improvements in certain directions, with the MBR self-training method achieving the best results. The Large Language Model also discusses the challenges and potential avenues for further research in the field of chat translation.
%U https://aclanthology.org/2024.wmt-1.105
%P 1031-1037
Markdown (Informal)
[Exploring the Traditional NMT Model and Large Language Model for Chat Translation](https://aclanthology.org/2024.wmt-1.105) (Yang et al., WMT 2024)
ACL
- Jinlong Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Zhiqiang Rao, Shaojun Li, Yuhao Xie, Yuanchang Luo, Zheng Jiawei, Bin Wei, and Hao Yang. 2024. Exploring the Traditional NMT Model and Large Language Model for Chat Translation. In Proceedings of the Ninth Conference on Machine Translation, pages 1031–1037, Miami, Florida, USA. Association for Computational Linguistics.