基于自建语料库的轨道车辆实操口译模块特异性与问题诊断研究——以2025轨道车辆技术国际训练营三大模块为例

A Study on the Specificity and Problem Diagnosis of Practical Interpretation Modules for Rail Vehicles Based on a Self-Built Corpus—A Case Study of the Three Core Modules of the 2025 International Training Camp on Rail Vehicle Technology

摘要: 笔者基于2025年轨道车辆技术国际训练营英语陪同口译实践，聚焦“模块A：受电弓检修与控制”“模块B：客室车门安装与调试”“模块C：车辆整车故障排查与处理”三大核心实操模块，自建模块化的专门语料库——“2025轨道车辆技术国际训练营实操口译语料库(RVTC-PI Corpus, 2025)”。该语料库总库容10,800形符，其中模块A 3600形符、模块B 3800形符、模块C 3400形符，属于单语语料库，内容严格限定于实操口译转写文本，针对当前轨道车辆口译研究中“泛化场景多、模块细分少”的现状，本研究采用“模块标注 + AntConc检索 + 人工统计”的分析方法，系统诊断各模块口译问题。研究发现三大模块问题特征分化显著：模块A因术语高度依赖实物语境，术语错误率最高(12.9%)，典型错误集中于“碳滑板”等部件名称；模块B因操作属性词的语境特殊性，指令误译率最为突出(9.8%)；模块C受代码与原因双重认知负荷影响，非流利频次远高于其他模块(9.2次/百词)。本研究验证了细分模块语料库在口译问题诊断中的有效性与精准性，据此提出“实物–术语”对照卡片、属性词表、代码–原因关联表等针对性训练对策，构建了“诊断–训练–验证”一体化的实操口译提升路径，拓展了语料库翻译学在技术口译领域的应用维度。本研究深度依托吉尔口译精力分配模型、语料库口译量化分析范式展开实证分析，通过标准化人工标注流程规避自动转写偏差，实现不同实操模块口译认知负荷、错误类型的差异化量化归因，弥补现有技术口译研究认知理论落地不足、操作流程不透明的短板。

Abstract: Based on the English liaison interpreting practice at the 2025 International Rail Vehicle Technology Training Camp, this study centers on three core practical modules: “Module A: Maintenance and Control of Pantograph”, “Module B: Installation and Commissioning of Passenger Compartment Door”, and “Module C: Fault Finding and Repair of Vehicle”. A self-built, modular, specialized interpreting corpus, the “2025 Rail Vehicle Technology Camp Practical Interpreting Corpus (RVTC-PI Corpus, 2025)”, was constructed, with a total size of 10,800 tokens (Module A: 3600; Module B: 3800; Module C: 3400). As a monolingual interpreting corpus, the study comprises exclusively English target-language texts from practical interpreting scenarios. In response to the prevailing issue in rail vehicle interpreting research, which often features “generalized scenarios and lacks module-specific analysis”, this study adopted a “module-specific annotation + AntConc retrieval + manual analysis” approach to systematically diagnose interpretation issues across the modules. The findings reveal distinct problem profiles for each module: In Module A, due to the high contextual dependency of terminology on physical objects, the terminology error rate was the highest (12.9%), with typical errors concentrating on component names like “carbon contact”. In Module B, attributed to the context-specific nature of operational attribute words, the instruction misinterpretation rate was most prominent (9.8%). Module C, influenced by the dual cognitive load of associating fault codes with their causes, exhibited a significantly higher frequency of disfluencies (9.2 instances per hundred words) compared to the other modules. This research validates the effectiveness and precision of a segmented module-specific corpus in diagnosing and interpreting problems. Consequently, targeted training countermeasures are proposed, including “physical object-term” reference cards, attribute word lists, and code-cause association tables. These contribute to building an integrated “diagnosis-training-verification” pathway for enhancing practical interpreting skills and expanding the application scope of corpus-based translation studies in the domain of technical interpreting. Drawing on Gile’s Effort Model and corpus-driven quantitative interpreting research paradigm, this study conducts empirical analysis, avoids automatic transcription bias through a standardized manual annotation process, realizes differentiated quantitative attribution of interpreting cognitive load and error types across practical modules, and addresses the shortcomings of insufficient application of cognitive theory and opaque operational procedures in existing technical interpreting research.