Exploring the traditional NMT model and Large Language Model for chat translation
arXiv:2409.16331
Abstract
This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on EnglishGermany (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.
7 pages, 6 Tables, WMT24