LLMs-based machine translation for E-commerce

• Published in: Expert systems with applications 2024-12, Vol.258, p.125087, Article 125087
• Main Authors: Gao, Dehong, Chen, Kaidi, Chen, Ben, Dai, Huangyu, Jin, Linbo, Jiang, Wen, Ning, Wei, Yu, Shanqing, Xuan, Qi, Cai, Xiaoyan, Yang, Libin, Wang, Zhen
• Format: Article
• Language: English
• Subjects: E-commerce domain; Fine-tuning; Large language models; Neural machine translation; Self-contrastive
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125087

Large language models(LLMs) have shown promising performance for various downstream tasks, especially machine translation. However, LLMs and Specialized Translation Models (STMs) are designed to handle general translation needs, they are not well-suited for domains with specialized terms and writing styles, such as e-commerce, legal, and medicine. In the e-commerce domain, the text often contains many domain-specific terms and keyword-stacked structures, leading to poor translation quality with existing NMT methods. To tackle these problems, we have collected two resources specifically for the e-commerce domain, including aligned Chinese-English bilingual terms and parallel corpus from real e-commerce scenarios for model fine-tuning. We propose an LLMs-based E-commerce machine translation approach(LEMT) which includes LLMs utilization, e-commerce resources collection, and tokenizer optimization. We conduct two-stage fine-tuning and self-contrastive enhancement based on general LLMs to enable the model to learn translation features in the e-commerce domain. Through comprehensive evaluations on real e-commerce titles, our LEMT methodology demonstrates superior translation quality and robustness, outperforming leading NMT models such as NLLB, LLaMA, and even GPT-4. •Existing universal translation models perform poorly in the e-commerce field.•Translation models based on LLMs improve translation capabilities more efficiently.•Resources based on e-commerce texts are conducive to e-commerce translation.•Two-step fine-tuning gradually enhance the translation ability of specialized domain.