The Structural Limitations of Machine Systems and the Financial Infeasibility of Dedicated Computational Environments 1. The Core Issue: The Disruption of Structural Analysis through Strict Glossary Enforcement (The 30-Year-Old Historical Precedent)         ・ The User’s Testimony: Over 30 years ago, a specialized operation mode forcing absolute adherence to user-defined glossaries was implemented in a machine translation (MT) system developed by Nippon Electric Company, Limited (NEC). This constraint stripped the system of its flexibility, causing a total collapse in its syntactic parsing (structural analysis) and resulting in highly corrupted target text.

・ The Modern Parallel: This historical failure directly mirrors the modern phenomenon known in academic literature as "Catastrophic Forgetting." When contemporary automated models are forced or heavily weighted to retain specific datasets, their broader structural generation capabilities degrade significantly. The fundamental limitation—where local constraints destabilize the global balance of the system—has remained unchanged for three decades.

2. The Practical Evaluation: The Illusion of Visibility Features

・ The Proposed Mechanism: A concept was considered to visually isolate and identify approved translation memory (TM) segments by enclosing them in heavy brackets (【】) upon generation. This approach was intended to protect structural logic while providing clear signaling to human reviewers, allowing for immediate quality audits without altering the internal computational weights of the system.         ・ The Critical Constraint: However, because automated systems remain fundamentally incapable of securely and simultaneously managing distinct, client-specific translation memories without experiencing memory degradation or data mixing, generating the clean baseline required for such visual signaling remains highly impractical.

3. The Financial Infeasibility: Dedicated Single-Client Server Deployment

・ The Computational Hypothesis: The possibility of bypassing general intelligence frameworks to deploy a simplified, dedicated server exclusively handling a single client's dataset (e.g., Amazon’s translation memory) was evaluated.         ・ The Economic Reality: This approach is structurally and financially unviable. Operating an isolated computational resource dedicated to a single client introduces massive fixed maintenance expenses and continuous operational costs, which cannot be sustained by project-based translation rates. Furthermore, stripping a model of its general intelligence reduces it to a rigid, legacy processing system, completely defeating the purpose of utilizing advanced computing.

4. Final Operational Conclusion ・ The Definitive Solution: Automated systems are structurally blocked by memory conflicts and economically blocked by high deployment costs. Therefore, relying on automated learning for client-specific adaptation is completely impractical.

・ The Optimal Workflow: The most reliable, accurate, and cost-free method for securing technical quality remains human-led control: utilizing standard translation memory databases within existing translation software, and performing manual global search-and-replace actions during the initial phase of production.

 #MachineTranslation #Localization #TranslationMemory #CATTools #AIQualityAssurance #Computational Linguistics #NaturalLanguageProcessing #Translatability #QualityControl #WorkflowOptimization