Work

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

Year: 2024

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.16331

Authors: 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

Institution: Huawei Technologies (China)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403789017

Cites:

Cited by: 1

Related to: 10