In the landscape of audiovisual adaptation, translating from Korean just happens. Often, due to a “lack” of direct resources, a pivot language —usually English—is used. However, this double-step process risks flattening the complex structures of formality and the cultural references inherent in the original text.
Should we be paying a premium for specialized Korean-to-Italian translation? Obviously. Do we actually do it? Absolutely not. Squid Game wasn’t just a global hit; it was a masterclass in how much we undervalue the people behind the subtitles.
Today, artificial intelligence (such as Claude) is changing the rules of the game. It is no longer just a simple machine translation (which often resulted in a patchwork of uncontrolled segments and pure nonsense), but about assisted re-elaboration that allows for quality control throughout the entire process. AI acts as a refined intermediary: by analyzing the original Korean text alongside the English pivot version, it can decode socio-linguistic nuances that a human translator, working only with the pivot language, might overlook.
Integrating AI into the workflow does not replace the adapter; rather, it enhances their research capacity, ensuring that every line maintains its original intent. Hopefully, we achieve a more precise and faithful delivery that respects the narrative richness of the source product. Technology is not a shortcut, but a tool to build stronger bridges between cultures.
Ultimately, we must remember the cost of invisibility: because our work is often perceived as “transparent”—the better it is, the less it is noticed—we must advocate for the value we bring. Quality is not a budget line item; it is a fundamental form of respect for the original creation.
For further reading on pivot language translation and the impact of large language models, you can consult the studies published by the Machine Translation Archive on neural translation quality for low-resource languages and analyses regarding LLM optimization for dubbing.
Prompt for Excel File Analysis
Copy and paste this prompt into Claude after uploading your Excel file:
“Act as an expert audiovisual translator specializing in Korean and European languages. We will analyze this Excel file containing three columns: [Column A: Original Korean Text], [Column B: English Pivot Translation], [Column C: Notes].
Your task is to analyze the text row by row, starting from the original language (Korean) and comparing it with the English pivot to identify discrepancies or loss of meaning. For each row (or block of rows), produce a structured output that includes:
Formality Levels: Analyze the Korean suffix system and the register used. Indicate if the English translation has correctly maintained the level of deference or social hierarchy and suggest the most appropriate adaptation for the Italian target audience (e.g., informal vs. formal/polite register).
Cultural Notes: Identify references to food, idioms, traditions, or specific socio-cultural concepts. Briefly explain the original meaning and evaluate if the pivot translation has rendered the cultural impact correctly or if it requires more accurate localization.
Repetitions and Characterization: Highlight recurring lexical items or syntactic structures that define a specific character’s voice. Note whether these repetitions serve to mark personality or character development within the series.
Finally, propose an optimized translation in Italian that takes into account all the notes above.”
Concluding Note
This is not a critique of my colleagues working from Korean; it is a critique of a market increasingly driven by standardization, LQA scores, and rigid success metrics. Currently, the industry path of least resistance is to streamline and flatten content through English, rather than investing in the specialized workflows required to honor and value linguistic diversity. My work is not a disruption of the craft, but a pragmatic solution to an existing problem and a challenge to a deeply entrenched—and often flawed—standardized workflow.
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