Neural Machine Translation for Multilingual Human–Machine Collaboration in Smart Factories Supporting the <scp>IIoT</scp>

ABSTRACT With the rise of the Industrial Internet of Things (IIoT), achieving effective multilingual human‐machine collaboration has become a significant challenge in smart factory environments. This paper introduces xTUNE, a consistency‐regularized neural machine translation (NMT) model, and presents a comprehensive multilingual translation system that incorporates data cleaning, augmentation, and embedding construction. xTUNE employs a two‐phase fine‐tuning mechanism to significantly improve cross‐lingual transferability and generalization. Experimental results show that the multi‐pivot strategy improves BLEU scores by 3.8 percentage points over the English‐pivot baseline and enhances POS and QA task performance by up to 2.6 percentage points. Furthermore, a cross‐domain wireless sensing model based on adversarial transfer learning is developed to enable multilingual adaptation under low‐resource scenarios, and is integrated into a ROS‐based human–computer interaction (HCI) framework.