Translator Productivity under Domain Terminology Extraction across Patent Localization Workflows
Keywords:
Translator Productivity, Causal Modeling, Terminology Extraction, Patent Localization, Translation WorkflowsAbstract
The globalization of intellectual property has significantly amplified the demand for high-quality patent translation, necessitating advanced localization workflows to handle complex, multidisciplinary documents. Within these workflows, domain terminology extraction is widely hypothesized to enhance translator productivity, yet empirical evidence has historically relied on correlational rather than causal frameworks. This paper addresses this methodological gap by applying causal inference modeling to assess the true impact of automated domain terminology extraction on translator productivity in patent localization. By utilizing a structural causal model, we disentangle the treatment effect of terminology extraction from confounding variables such as translator experience, source text complexity, and domain familiarity. Through a controlled experimental design involving professional patent translators, productivity is measured using temporal and cognitive load indicators. The application of directed acyclic graphs facilitates the identification of causal pathways, revealing that pre-translation terminology extraction significantly reduces cognitive burden and editing time, though the magnitude of this effect is highly contingent on the algorithmic precision of the extraction tool and the structural density of the patent claims. The findings demonstrate that causal modeling provides a robust framework for evaluating technological interventions in language service provisioning. These insights hold substantial implications for the optimization of localization pipelines, resource allocation in translation management systems, and the design of next-generation computer-assisted translation environments.References
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