Translating Mongolian-to-English: My thoughts on AI
Introduction
As someone deeply passionate about cultural exchange and linguistic preservation, I’ve dedicated quite some time to translating Mongolian literature—particularly poetry—into English. Mongolian poetry is rich in metaphor, rhythm, and philosophical reflection, often reflecting the nomadic spirit and historical resilience of its people. Translating such works is no simple task; it demands a nuanced understanding of language, culture, and context. Recently, I’ve incorporated generative AI tools, such as ChatGPT, into my translation workflow, using them as powerful aids for generating initial drafts and exploring linguistic possibilities.
The journey of translating Mongolian poetry is as much about adapting the message as it is about preserving the integrity of its cultural and philosophical depth. Here, I’ll share my translation process, delve into how generative AI models function in the realm of language, and explore the future of AI in literary translation.
How Generative AI Models Assist Translation
Generative language models like ChatGPT are built on transformer architectures—a type of neural network designed for handling sequential data, such as language. These models are trained on vast datasets, comprising texts in multiple languages, literary styles, and contexts. Through training, the models learn patterns, relationships between words, and how these relationships vary across languages. This is what enables them to perform tasks like translation, summarization, and language generation.
When translating, models analyze the input text and map it to a probabilistic representation of equivalent meanings in the target language. This mapping is informed by patterns in the training data. For example, when translating the Mongolian line "Хатмал цөлд явхад хар ус амттай" (In a parched desert, black water tastes sweet), the model identifies the structure, syntax, and meaning of each word and predicts the most likely English equivalent for the entire phrase based on its learned linguistic patterns.
However, while these models excel at processing grammar and structure, their reliance on training data introduces certain limitations. AI lacks cultural awareness and cannot intuitively grasp metaphorical or symbolic meanings. For instance, "хар ус" (black water) in Mongolian poetry refers to water that is murky and barely potable—just enough to sustain life. In the context of the steppe, where water scarcity defines survival, this phrase carries a deeper emotional and cultural resonance. AI can identify the literal meaning but often misses the layered significance that connects this imagery to Mongolian perceptions of endurance and gratitude. It is in these subtleties that the human translator plays an irreplaceable role, ensuring that the cultural essence is faithfully conveyed to the target audience.
My Translation Process: Using Generative Pre-trained Transformer
1. Draft Creation Using Generative AI
The process begins by feeding the original Mongolian text into an AI model, which produces a first draft in English. This draft provides a useful baseline, accurately conveying grammatical structure and basic meaning. For instance, generative AI can reliably translate straightforward phrases like "Жаргал ховор цагт жаргалын бага нь амттай" (In times when happiness is rare, even a little is delightful).
AI also aids in romanizing the text, providing consistent transliteration for Mongolian letters into the Latin alphabet. For example, Х becomes "kh," Ө becomes "ö," and Ү becomes "ü." This step ensures phonetic accuracy, especially for readers unfamiliar with the Cyrillic script.
2. Extensive Human Refinement
The AI-generated draft is just the starting point. The bulk of my work involves refining the draft to capture cultural nuance, philosophical depth, and poetic rhythm. Mongolian poetry often employs layered metaphors and symbolic imagery tied to the nomadic way of life. Phrases like "өрөмт нуурын ангирт өглөө" (the creamy lake’s morning filled with wild ducks) evoke specific cultural and natural landscapes that require careful contextualization.
During this phase, I rely on my personal glossary of Mongolian terms and phrases to ensure accurate interpretation. For instance, words like aaruul (dried curd) or ger (yurt) are left untranslated, with accompanying translator’s notes explaining their cultural significance.
3. Adding translator's notes
Translator’s notes are an integral part of my process, serving as a bridge between the original text and its translated form. These notes provide readers with the cultural, historical, and philosophical context needed to fully appreciate the nuances of the text. For instance, when translating a line like "Үүлэнд хэлбэр байхгүй" (Clouds have no shape), I include a note explaining how this reflects Mongolian views on impermanence and nature’s resistance to rigid definitions. Such interpretations go beyond the literal meaning, offering insights into the worldview embedded within the poetry.
Through these notes, I’ve come to a significant realization: it is impossible to achieve a perfect translation. Every linguistic choice involves a trade-off, where retaining one element may mean losing another. Mongolian poetry, steeped in metaphor and cultural resonance, often conveys meanings that simply do not have direct English equivalents. For example, хар ус (black water), while literally referring to murky water, carries connotations of survival, scarcity, and resilience that resonate deeply with the nomadic experience. Without contextual notes, these layers of meaning might be lost on the reader.
Rather than striving for an unattainable perfection, I embrace translator’s notes as a way to complement the translation. They transform the process from a simple linguistic exercise into a gateway to the poet’s world, ensuring that the text’s richness and depth are not only preserved but also accessible to a global audience. By acknowledging the limitations of translation and providing this supplementary layer, I aim to create a more meaningful and immersive experience for readers.
Translation and Romanization
After completing the poetic translation, I often include a word-for-word breakdown of the original Mongolian text. This allows readers to see how individual words and phrases are interpreted, providing transparency in the translation process. Romanization further aids readers in grasping the phonetics of the original text.
For instance, the line "Гээсэн сүх шиг жаргалыг" is broken down as:
- Гээсэн (geesen) = lost
- Сүх (sükh) = axe
- Жаргалыг (jargalyg) = happiness (accusative)
This meticulous approach ensures that the poetic and linguistic layers of the original text are preserved and accessible.
Challenges in AI Translation
While generative AI has made significant strides, it is not without limitations. The primary challenges include:
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Cultural Context: AI often struggles to interpret culturally specific metaphors, idioms, and symbols. For example, "хар ус" (black water) in Mongolian poetry carries cultural connotations that extend beyond its literal meaning.
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Philosophical Depth: Mongolian poetry frequently explores existential themes, such as the transient nature of happiness or the human relationship with nature. AI models may provide a grammatically correct translation but lack the ability to convey these deeper meanings.
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Rhythm and Aesthetics: The musicality and rhythm of Mongolian poetry—often achieved through repetition and alliteration—are difficult to replicate in English without significant human intervention.
The Future of AI in Literary Translation
Looking ahead, I see generative AI as an increasingly valuable tool in the field of translation. Models like GPT-4 have already improved drastically over earlier iterations, demonstrating greater fluency and contextual understanding. As AI continues to evolve, initial drafts will likely become more accurate and nuanced.
However, the quality of translations will always depend on the data these models are trained on. While widely studied languages like English, French, or Chinese benefit from extensive corpora, less-documented languages—such as Mongolian or ancient languages like Aramaic—may face challenges due to limited training data. Ensuring that AI models incorporate diverse and high-quality datasets will be essential for advancing their capabilities.
While I am optimistic about the future of AI-assisted translation, I remain cautious about its ability to capture the depth and richness of poetry. Literary translation is as much an art as it is a science, requiring empathy, creativity, and cultural insight—qualities that remain uniquely human.
Conclusion
Translating Mongolian poetry is a labor of love, one that combines the precision of computational tools with the creativity and cultural understanding of the human translator. Generative AI models like ChatGPT have transformed my workflow, providing an efficient starting point for translations and enabling me to focus on the finer details of interpretation and refinement.
Yet, the essence of poetry lies in its ability to resonate emotionally and philosophically—a task that no algorithm can fully accomplish. The future of AI in translation is promising, but its greatest value will always lie in its ability to support, not replace, the human touch. Through this collaborative process, I hope to continue sharing the beauty of Mongolian literature with the world, ensuring its timeless themes of happiness, suffering, and resilience transcend linguistic and cultural boundaries.
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