Abstract

Information technology service management (ITSM) systems generate large volumes of unstructured incident descriptions. They frequently include multilingual content, code-switching, informal language, and domain-specific terminology. These characteristics make automated text processing substantially more complicated and limit the applicability of conventional machine translation solutions, particularly in environments subject to strict data privacy and air-gap constraints. This paper presents a system-level reproducibility study of a deterministic two-stage cascaded neural machine translation (NMT) pipeline for normalizing multilingual IT incident text in resource-constrained, air-gapped environments. The study evaluates a sequential RU→EN and LT→EN translation strategy specifically selected to bypass unreliable language identification, enabling stable processing of code-switched incident descriptions. A system-level processing pipeline, which includes text normalization, segmentation, deduplication, adaptive batching, and language-aware data flow optimization, is analyzed to assess its impact on reducing redundant inference operations. The methodology is evaluated on a real-world ITSM dataset comprising 84,285 incident records. An incremental experimental design is used to isolate the specific contributions of computational and data-flow optimizations. Translation quality is assessed using BLEU and COMET metrics against expert reference translations produced via a primary translation and subsequent cross-verification by a second domain expert to ensure linguistic and technical consistency. The results indicate that a cascaded NMT architecture combined with systematic data-flow optimization provides a reproducible and privacy-preserving framework for multilingual IT incident text normalization, effectively supporting downstream analytical tasks in constrained operational ITSM environments.

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