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Alibaba researchers are investigating large language models for e-commerce translation.

A group of researchers from the Alibaba Group, the Chinese company behind the Alibaba online stores alibaba.com and aliexpress.com, conducted experiments to test neural machine translation (MT) model behavior and large language model (LLM)-based MT behavior on e-commerce content after specific training. The results were published in a paper (download) on March 6, 2024.<br><br>https://slator.com/alibaba-researchers-probe-large-language-models-e-commerce-translation/

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Alibaba researchers are investigating large language models for e-commerce translation.

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  1. Alibaba researchers are investigating large language models for e-commerce translation www.slator.com

  2. A group of researchers from the Alibaba Group, the Chinese company behind the Alibaba online stores alibaba.com and aliexpress.com, conducted experiments to test neural machine translation (MT) model behavior and large language model (LLM)-based MT behavior on e-commerce content after specific training. The results were published in a paper (download) on March 6, 2024. In their publication, Alibaba researchers Kaidi Chen, Ben Chen, Dehong Gao, Huangyu Dai, Wen Jiang, Wei Ning, Shanqing Yu, Libin Yang, and Xiaoyan Cai argue that current machine translation models often neglect domains characterized by specialized writing styles, such as e-commerce and legal documents. In an effort to enhance model performance, the researchers embarked on testing a multistep training methodology tailored to address the morphological, lexical, and syntactical features unique to e-commerce text. These features encompass elements such as keyword stacking and varying lengths of product descriptions, both long and short. www.slator.com

  3. E-Commerce Peculiarities According to the researchers, traditional MT methods may lead to issues in translating e-commerce content, such as low accuracy and occurrences of keyword omission and duplication. In contrast, they argue that their "general-to-specific" (G2ST) approach to model training yields superior results. The G2ST methodology uses two-phase fine-tuning and contrastive enhancement steps to enhance results (contrastive enhancement is a method in which different candidate translations are compared and the model learns to choose the better translation). It works by “transferring” a general MT model to an e-commerce-specific MT model. www.slator.com

  4. Slator is the leading source of news and research for the global translation, localization, and language technology industry. Our Advisory practice is a trusted partner to clients looking for independent analysis. Headquartered in Zurich, Slator has a presence in Asia, Europe, and the US. www.slator.com

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