Translation is booming like never before!
More and more content is flooding the net which needs to be accessible all over the world at any time.
Many companies operate at an international level and therefore multilingually.
These developments mean that the demand for translation services is greater than ever.
Luckily, the advancing development of AI-based tools means that translation processes can be partially or even largely automated.
Using AI as a translator
AI can be integrated into translation processes in several ways. The three main methods are:- Machine Translation (MT): Fully automated translation of a text by an AI model (translation engine). Multilingual, aligned textual data is used to train the engine.
- Quality Risk Estimation (QRE): Quality assessment of a translation using an AI model. The model assigns a score to each translation. If the score is below a certain threshold, the translation must be reviewed and potentially corrected.
- Automatic post-editing (APE): Automatic improvement of machine-generated translations to correct errors and improve translation quality.
Speeding up translation processes
Many companies simply use machine translation. This leads to a change in the role of the human translator, who must no longer translate from scratch, instead focusing on correcting machine-generated translations. This makes the overall translation process more efficient.
The nuts and bolts of modern machine translation
MT works best with a deep neural network of the so-called “transformer” architecture.
This neural net is trained with domain-specific, high-quality, multilingual data.
Once the model has been trained, requests can be processed quickly and cost-effectively.
The output of the finished engine is accurate, domain-specific, and reliable.
Can LLMs (Large Language Models) work as translators?
LLMs, such as GPT, are huge, deep neural networks, typically transformers.
These are trained with vast amounts of data from various domains to handle a multitude of language-based tasks.
Their flexibility allows a wide range of applications, however, LLMs usually cannot compete with models specifically designed for each individual task.
In addition, they are often less efficient, and processing large amounts of data is more expensive when compared to specialized models.
As a result, LLMs are not the best choice for machine translation at the moment.
However, this can change quickly, given the current speed of development in the AI field.
Give me more AI!
It is possible to extend the “classic” AI-supported translation process by adding Quality Risk Estimation (QRE) or Automatic Post-Editing (APE).
A combination of both methods is also an option.
In this case, QRE recognizes low-quality translations, which are then automatically optimized by APE.
This eliminates the need for human translators to perform many simple proofreading tasks, making the entire process more efficient without compromising translation quality.
Such a combination of QRE and APE can be realized using LLMs.
The ideal solution can depend on the training data and technical resources available.
Conclusion
How a company can optimize its translation processes depends on many factors. These include, for example, the data at hand, the desired output quality, which technical resources are available and, of course, the budget.
Often, an AI-based approach can be worthwhile, especially when dealing with large amounts of data.
This enables companies to respond to international markets faster and more cost-effectively without compromising the quality of their translations.
Do you want to increase the efficiency of your translation processes with AI? Contact us, and we will show you how to do it!
