Accelerate projects and reduce costs by leveraging AI for the best machine translation process.
Generative Artificial Intelligence (AI) has become a cornerstone in the translation industry. It drives efficiency and creates more cost-effective workflows for language service providers (LSPs).
As AI continues to evolve, its role in machine translation is expanding. But, even the most advanced machine translation (MT) systems still require human post-editing for optimal results.
Why is that? While AI has made impressive strides, it has not yet mastered perfect translation output across all use cases. However, AI is an exceptional evaluation tool, accelerating the quality assurance phase and improving translation efficiency.
Let’s take a deeper dive into where we stand with leveraging AI for the best machine translation processes.
What’s Neural Machine Translation (NMT)?
For a while now, there have been three types of computer-aided translation systems:
Neural Machine Translation (NMT) has marked a breakthrough in AI-driven translation, though.
NMT systems leverage deep learning to recognize patterns and context in source texts, producing translations that are more natural and fluid.
It learns from every task, improving accuracy over time when there’s a feedback loop in place with the changes humans have made to the generated translation.
Now, with the help of Large Language Models (LLMs), NMT has reached new heights of sophistication.
The Large Language Model (LLM)
Large Language Models (LLMs) like GPT-4 use deep learning algorithms trained on vast amounts of multilingual data to understand, summarize, generate, and translate text.
Unlike traditional models that rely on rules or statistical probabilities, LLMs excel at understanding the nuances of language, offering context-based translations that are increasingly human-like.
The LLM’s ability to handle complex tasks, including text summarization, sentiment analysis, and conversational AI, adds significant value to machine translation.
LLMs recognize intricate connections between words, making them capable of generating context-aware, high-quality translations across multiple languages.
But despite their potential, we are still not at the stage where translations can be fully automated with flawless accuracy.
AI-generated translations continue to require human oversight—especially for high-stakes content like legal, medical, and technical documentation.
AI’s Role in Translation Quality Assurance
While AI can generate vast quantities of translated content quickly, there’s a trade-off when it comes to quality. Even the most advanced LLMs can produce translations that are less than perfect—especially in low-resource languages where data for training is sparse.
Microsoft’s ongoing research and more recent comparisons of ChatGPT 4.0 to traditional MT engines found that GPT models produce competitive translation quality for high-resource languages (like English, Spanish, and Chinese).
However, for low-resource languages, GPT-based models still underperform due to limited data. For these languages, human expertise remains crucial to ensure accuracy and clarity.
So, where AI’s greatest contribution to the translation industry currently lies is evaluation.
Rather than fully automating translation, AI tools are now used to evaluate the quality of translations before post-editing by human linguists.
This hybrid approach—combining AI evaluation with human expertise—strikes the right balance between speed and precision.
The Hybrid Approach: NMT + LLM + Human Post-Editing
So, the most effective machine translation relies on the integration of neural machine translation (NMT), large language models (LLMs), and human post-editing (PE).
This hybrid method offers a streamlined and cost-effective solution while ensuring high translation quality.
It allows us to update existing translations with speed, security, and cost efficacy.
Only when large portions of a text have never been translated before or when a new translation does not have to match, word for word, an existing translation, can NMT and LLM help produce translations fast and cheap.
But if zero quality assessment has been done, by a human or an algorithm, there’s always going to be more risk in NMT/LLM output than in human reviewed TM output.
Here’s how it works:
- NMT: First, neural machine translation generates the initial draft.
- LLM Evaluation: Then, an LLM assesses the translation, evaluating its accuracy and fluency. The AI identifies potential errors and flags areas for improvement. We evaluated the machine evaluations with humans, and we learned that only fine-tuned LLMs can reduce the risk. Fine-tuned LLMs can be really helpful. Generic LLMs can give a false feeling of security.
- Post-Editing: Finally, a professional linguist reviews the translation, refining it to meet the specific nuances and requirements of the target language and context. It won’t take long, though, before LSPs move to automated PE.
AI’s ability to quickly evaluate and provide feedback on translations saves time and reduces human intervention to where it’s most needed. It picks the best translation from multiple options, explains errors, and offers suggestions.
While it isn’t foolproof, AI evaluation enhances the overall workflow, allowing translators to focus on what needs to be fixed rather than starting from scratch.
Find The Best Machine Translation Solutions for Your Needs
When approaching a translation project, there’s no one-size-fits-all solution. The right approach depends on your specific needs—whether it’s a human-only process for nuanced texts or an AI-assisted process for high-volume, less-sensitive content.
There are flexible translation solutions that can be tailored to your unique requirements. These services range from human translation for premium quality to AI-assisted machine translation for faster more cost-effective projects.
The key is in finding the right balance between human and machine involvement based on the complexity and criticality of your content.
