How does AI translation work?
AI is a broad term for algorithms that help machines perform tasks such as recognising objects, understanding language or solving complex problems. However, as AI technology developed, many different subsets appeared.
Some AI subsets are evolutions of previous AI technologies, developed to improve performance and speed. However, others are more than just improved versions: they have been specialised to handle different tasks. The most sophisticated AI tools for translation now use combinations of these subsets to achieve the best results.
Let’s look at types of AI that are currently used in translation tools:
Machine learning
Machine learning is a broad subset of AI that learns by looking at examples of data, getting better at tasks over time. Many applications that we use today were developed using machine learning. Tools like YouTube, Amazon and Netflix use machine learning to suggest media that you might like based on your past choices. Google Search can now handle billions of searches every day thanks to this type of AI, and it’s learning to be better with every search.
Machine learning is key to translation tools that use AI. It's used to analyse vast amounts of text and audio data to identify patterns in different languages. This complex process requires fast computer processing speeds and extensive data storage capacities. By analysing and storing data for future reference, machine learning tools can learn how structures and words compare between various languages, improving in accuracy and fluency over time.
Deep learning
The advent of advanced machine learning methods led to the development of deep learning neural networks. Deep learning networks are inspired by the structure and function of the human brain and are constructed like stacks of connected brain cells, called neurons. They translate between languages by analysing and ‘learning’ from huge data sets.
When used in translation applications, the first layer of the network of neurons receives information from the source audio or text. Then, each consecutive layer processes the information before passing it to the next layer. Finally, the output layer uses the combined processing to deliver the translation.
Having multiple layers helps the network to handle the intricacies and nuances of human language and to deliver better results than simpler algorithms. As the network learns from each task, it becomes increasingly accurate over time.
Neural machine translation
Deep learning neural networks are at the heart of neural machine translation (NMT), an advanced form of AI translation technology.
NMT models improve through a rigorous training phase. During this phase they are exposed to and learn from vast amounts of data across all the languages they are designed to translate. As they progress, they are iteratively adjusted to minimise translation errors, effectively ‘learning’ from their mistakes and ‘remembering’ successful outcomes. This continuous refinement process allows NMT to provide highly accurate and contextually relevant translations.
Generative AI
Generative AI creates unique output that resembles basic communications created by humans. It’s based on deep learning neural networks, which are trained with huge amounts of data, and is particularly useful for translation applications.
The use of generative AI has significantly advanced AI language applications. It creates translations that are closer to natural language than previous AI networks.
Natural Language Processing (NLP)
Language is full of nuances and often uses more than just words to convey emotion and meaning. NLP takes AI translation even closer to human translation because it’s designed to recognise meaning, emotion and intent. It’s used to create two-way interactions, creating a more human-to-human type of communication experience than any other type of AI.
Generative AI vs NLP
Despite the rapid advancement of generative AI solutions, the human-like ‘thinking’ of NLP is behind the best translation applications. NLP enables machines to understand, interpret and generate human language with unprecedented accuracy and sophistication, even recognising subtle emotion cues and intentions.
Conversational interfaces, such as virtual assistants, can be built using NLP networks to provide an enhanced user experience in multiple languages. They can mimic a conversation with a real human operator by understanding and responding to questions in a natural way and recognising the emotional content of queries.
The demand for interactions with AI that ‘feel’ like communicating with a real human means that networks combining generative AI with NLP are becoming popular. Generative AI is used to create the content that users request, while NLP focuses on understanding the meaning behind the input it receives.
Hybrid translation solutions
Some businesses choose a hybrid solution. This might entail using AI to translate large amounts of content quickly, and then professional translators to edit and improve the quality.
If you’d like advice on how a hybrid solution could improve your workflow, contact us to discuss your needs.
AI video translation
AI video translation can be used to translate the audio from video content to create subtitles or audio tracks for dubbing.
Find out more: AI video translation
AI voice translation gadgets
This type of app can be used to translate spoken words and phrases. AI voice translation gadgets are useful for communicating in real-time.
Find out more: AI voice translation gadgets
The drawbacks of AI translation
While there have been significant advancements in AI language technology, there are still substantial drawbacks to using it:
Contextual understanding
AI translation tools still struggle to understand context, slang or the cultural nuances that are present in the source text.
Industry-specific terminology
AI translation tools are prone to mistakes when translating technical, scientific or industry-specific language. If your organisation uses any of these types of terminologies, it’s important to check that they are translated accurately and that translations remain consistent across all content. Mistranslations of terminology can cost more than time and money: they damage credibility and even have legal implications.
Accuracy
The quality of AI translation output varies depending on factors such as the language pairs, the complexity of the text and the specific AI tool used. AI translation tools still have a long way to go when compared to human translators.
Security
Some AI translation tools don’t store or process data securely during the translation process.
Learning issues
AI translation tools are ‘trained’ by inputting large data sets for analysis. Inaccuracies present in this information, such as gender or cultural biases, can influence the translations produced by the AI model.
Our translation services
Our translators work in more than 200 languages and countless dialects. We can match you with a professional who specialises in your industry, giving them insight into how you communicate with your customers. They can seamlessly integrate with your existing workflow, edit your AI translations or provide a full translation service.
If you’re looking for a tailored hybrid translation solution, we can help. Combining the speed of AI translation with the accuracy of our translators means that you’ll enjoy peace of mind about quality while your workflows are faster than ever before.
