Exploring the traditional NMT model and Large Language Model for chat translation
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916410601504768 |
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| author | Yang, Jinlong Shang, Hengchao Wei, Daimeng Guo, Jiaxin Li, Zongyao Wu, Zhanglin Rao, Zhiqiang Li, Shaojun Xie, Yuhao Luo, Yuanchang Zheng, Jiawei Wei, Bin Yang, Hao |
| author_facet | Yang, Jinlong Shang, Hengchao Wei, Daimeng Guo, Jiaxin Li, Zongyao Wu, Zhanglin Rao, Zhiqiang Li, Shaojun Xie, Yuhao Luo, Yuanchang Zheng, Jiawei Wei, Bin Yang, Hao |
| contents | This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English$\leftrightarrow$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the traditional NMT model and Large Language Model for chat translation Yang, Jinlong Shang, Hengchao Wei, Daimeng Guo, Jiaxin Li, Zongyao Wu, Zhanglin Rao, Zhiqiang Li, Shaojun Xie, Yuhao Luo, Yuanchang Zheng, Jiawei Wei, Bin Yang, Hao Computation and Language Artificial Intelligence This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English$\leftrightarrow$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. |
| title | Exploring the traditional NMT model and Large Language Model for chat translation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.16331 |