The widespread adoption of machine translation has greatly enhanced translators' productivity, yet it has also brought about a range of issues. Machine translation excels at processing texts with standard structures, general terminology, clear context, and low cultural or emotional load.
However, when it comes to culturally loaded words, internet slang, and other expressions with deep meanings or high contextual dependency, it often fails to accurately convey the intended meaning, resulting in rigid and confusing translations.
Short drama subtitles are a perfect example—they rely heavily on cultural context and emotional resonance, making it absolutely inadequate to rely solely on machine translation. So, how can we appropriately use machine translation for short drama subtitles?
Strengths of Machine Translation
Machine translation performs well in handling informational and explanatory dialogues, such as background narration, plot-advancing commentary, and fact-based exchanges between characters. These types of sentences are relatively standard and primarily aim to convey information with minimal emotional nuance, making them suitable for machine processing.
Moreover, machine translation is quite effective with everyday, generic dialogues. High-frequency phrases like “你好吗？”（How are you?） or “明天会下雨吗?” (Will it rain tomorrow) have simple structures and clear meanings. Such sentences are extensively covered in training data, allowing machine translation to produce accurate and natural-sounding translations.
Finally, for producers looking to quickly enter overseas markets and gain a first-mover advantage, machine translation offers significant benefits. It can process large volumes of subtitles in a very short time, far surpassing human speed, making it a highly efficient solution.
Limitations and Risks of Machine Translation
Machine translation struggles with culturally loaded expressions. It cannot grasp the meaning behind historical allusions, idioms, or customs rooted in specific cultural contexts, often resorting to literal translation that loses the original sense.
Internet buzzwords and humorous expressions are also major challenges. For example, the phrase “你可真是个老六” (nǐ kě zhēn shì gè lǎo liù) can have different meanings depending on the context.
In gaming, it might describe an unskilled or sneaky player (equivalent to “newbie” or “camper”). Among friends, it could be used teasingly to mean someone who is funny (like “troublemaker”). If translated literally without context, the audience would be confused.
The same issue arises with emotionally charged or suggestive dialogue. For instance, when a character says sarcastically, “You’re such a good person!”, machine translation fails to detect the irony, producing a literally correct but emotionally misplaced translation that ruins the dramatic tension.
Wordplay, including puns and homophonic phrases, is another critical weakness. A classic example is the Chinese bicycle ad: “骑乐无穷” (qí lè wú qióng), a pun on the idiom “其乐无穷” (endless fun). Machine translation typically captures only one layer of meaning, completely missing the humorous effect and making the storyline seem absurd.
The Human-Machine Collaborative Approach
High-quality subtitle localization should never rely entirely on machine translation. Bridging the gap between text and culture requires human guidance—machine translation should serve as a tool, not the decision-maker. True localization involves not just language transformation, but also recreation and cultural adaptation.
Therefore, producers should establish a human-centric workflow assisted by machine translation. Start by using machine translation to generate a draft quickly, then have localization experts who are fluent in both languages and cultures refine and creatively adapt the content to enhance audience comprehension.
If budget constraints exist, leveraging community efforts through crowdsourcing or fan-based proofreading can also help capture the most authentic and idiomatic expressions, enabling more flexible and relatable localization.
In conclusion, the future of short drama localization lies in balancing speed and quality. By combining the efficiency of machine translation with human cultural insight, we can build a collaborative human-machine workflow—and only then achieve a genuine leap from text translation to cultural experience.
