Exploring the traditional NMT model and Large Language Model for chat translation

Autor:	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
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 6 Tables, WMT24
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.16331 Zobrazit plný text záznamu View this record from Arxiv