Exploring the traditional NMT model and Large Language Model for chat translation
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Abstract (arXiv)
This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on EnglishleftrightarrowGermany (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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