Year: 2025
Type: article
Abstract: Cross‑lingual natural language processing has moved from academic curiosity to industrial necessity. Modern software development is global, with teams distributed across continents and documentation written in a handful of lingua franca. Terms like autoscaling group, immutable infrastructure or zero‑trust network segmentation emerge in English‑language cloud platforms and quickly travel to DevOps manuals, system administration runbooks and cyber‑security guidelines. Yet those terms often lack precise equivalents in other languages, and literal translations can lead to misunderstandings. This article examines how to build multilingual term mapping models that align industry terminology across languages, with a focus on the information‑technology (IT) domain. We describe a layered methodology that combines statistical term extraction, semantic alignment of cross‑lingual embeddings, iterative fine‑tuning of neural translators and careful curation of expert glossaries. A large‑scale case study covers cloud computing, DevOps practices and cyber‑security, analyzing the effectiveness of different approaches on English–Russian and English–Kazakh pairs. Experiments reveal that the proposed hybrid model reduces errors by over 20 percentage points compared with dictionary‑based baselines, increases term coverage to over 80 % and substantially cuts manual post‑editing time.
Source: InterConf
Author: Abay Mukhtaruly
Institution: Astana IT University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7084433784