The development of AI-supported translation tools has made enormous progress in recent years. Systems such as ChatGPT or DeepL now offer users convincing solutions for fast and accessible machine translation (MT). But how good are these tools really? And can they keep up with human translators? In this article, we take a look at the current possibilities and limitations of AI translations and compare the results with human expertise.
How exactly do AI translations work?
AI translations are based on neural networks that analyze huge amounts of text data and recognize patterns in order to understand and translate language. Most modern translation systems use so-called transformer models, a special architecture for machine learning that can process context information efficiently. These models go through two main phases:
- Training: The system is trained with millions of sentence pairs in different languages to learn relationships between words, phrases and contexts.
- Inference: During use, the system analyzes the input text, creates an internal representation of the context and generates a translation based on the learned patterns.
In addition, many tools use techniques such as machine learning, language models and context recognition algorithms to deliver better quality translations. However, there are limits, as these systems do not “understand” like a human, but only work on the basis of statistical probabilities.
What AI translation tools are currently available on the market?
The selection of AI translation tools is large and constantly expanding. Here are some of the most popular options:
- DeepL: Known for its high translation quality and simple user interface. Particularly strong in European languages.
- ChatGPT: Originally a language model that is now also used as a translation tool. Flexible and context-sensitive, but with weaknesses in technical details.
- Google Translate: One of the best-known tools, available in almost all languages, but with sometimes inconsistent results.
- Microsoft Translator: Integrated into many Office products, ideal for the rapid translation of documents.
Amazon Translate: Particularly interesting for companies that need to translate large volumes of text in real time.
The advantages and disadvantages of AI translations
Advantages
1. speed: AI systems deliver translations in a matter of seconds. Example: A contract with 10,000 words can be translated within minutes.
2. cost savings: MT solutions can be more cost-effective than human translations, especially for large volumes of text.
3. scalability: Ideal for companies that need to serve several languages simultaneously. Example: A global marketing campaign can be translated into 20 languages simultaneously.
4. accessibility: Many tools are available free of charge or at low cost.
5. learning ability: Modern AI systems can be continuously improved by training them with feedback and new data.
Disadvantages
1. lack of context: AI systems have difficulty interpreting the context of a text correctly. Example: An ambiguous sentence such as “The king spoke to his people” could be translated differently depending on the context.
2. stylistic restrictions: Particular linguistic nuances and cultural conventions are often ignored. Example: Marketing texts that contain cultural allusions are often translated literally and not idiomatically.
3. fluctuations in quality: The results vary greatly depending on the type of text, subject area and target language. Example: A technical text is often translated correctly, but a poetic text is rarely translated satisfactorily.
4. post-editing required: Human post-editing is often necessary to achieve a high-quality translation. Example: A legal text often requires precise knowledge of specialist terminology, which AI tools do not always provide.
The importance of terminology and terminology management for secure AI translations
A decisive factor for the quality of AI translations is the consistent use of specialist terminology. Especially in industries such as law, medicine or technology, where precise terms are essential, well-maintained terminology management can make all the difference.
Why is terminology important?
AI systems are often unable to distinguish between synonymous or ambiguous terms if there is no clear specification. Example: In the technical field, the word “line” can mean “cable”, “management” or “pipe”, depending on the context. Without a terminology database, the AI may make the wrong choice.
How does terminology management support AI?
- Specification of glossaries: By integrating specialist glossaries into the AI system, translations can become more precise.
- Feedback loops: By regularly updating the terminology, the AI can react better to new requirements.
- Improving consistency: Terminology management ensures that terms within a text are translated consistently.
Quality assurance of AI translations with post-editing
Even the best AI systems do not always deliver perfect results. This is where post-editing comes into play, where human translators revise the AI’s output to correct errors and improve readability.
What does post-editing involve?
– Correction of grammar and spelling errors: Even advanced AI tools make grammatical or punctuation errors.
– Adaptation to context: Humans can better assess the cultural and stylistic context of a text.
– Checking terminology: Technical terms are checked and adapted if necessary.
– Improving style: Post-editing ensures more appealing language, especially for creative or advertising texts.
Examples from practice
– Technical manuals: After machine translation, all technical terms are checked to ensure that they are correct and consistent.
– Marketing materials: A complete stylistic revision is often necessary to convey the message effectively.
In or from which languages do AI translations work particularly well?
Modern AI translation systems are particularly good at processing languages that have large and well-annotated data sets. Here are some examples:
Languages with high translation quality
1. English: As the most widely spoken language on the internet, English provides an excellent basis for AI models.
2. European languages: Languages such as German, French, Spanish and Italian often deliver high-quality results.
3. Asian languages (partially): Chinese, Japanese and Korean are increasingly better translated, especially by specialized tools.
Languages with challenges
1 African languages: Due to the lack of training data, translations in languages such as Zulu or Hausa are often inaccurate.
2. Indigenous languages: Many indigenous languages do not have enough data to achieve good results.
3. Complex grammar: Languages such as Hungarian or Finnish, which have complex grammatical structures, often pose a challenge for AI systems.
Practical test: Can an AI translation keep up with that of a human?
In an experiment, we tested DeepL and ChatGPT for specialist translations in the fields of law, marketing and technology. The translations into French and Polish were evaluated by native-speaker post-editors based on five error categories:
– Consistent terminology
– Correctness and completeness
– Style
– Meaningfulness
– Language conventions (grammar, spelling, punctuation, etc.)
The results
- DeepL: Stylistically better, but problems with completeness (e.g. missing paragraphs in the legal text). In Polish, the passive voice is used unusually often.
- ChatGPT: Better terminological consistency, but inconsistent forms of address and translations that distort the meaning of complex texts.
Neither DeepL nor ChatGPT were able to fully capture the context of the source texts. For marketing texts in particular, the results were so unsatisfactory that a complete retranslation was necessary. Nevertheless, both systems offer solid approaches that can be improved by human post-editing.
DeepL vs. ChatGPT: A detailed analysis
You can find the full analysis of the experiment in our blog article “MT and AI-supported translations in practice: The duel DeepL vs. ChatGPT”.
Conclusion
AI translations are a valuable tool that can score particularly well with simple texts and large volumes of data. However, human translators remain indispensable for specialist translations or stylistically demanding texts. However, the combination of machine translation and human post-editing could offer the optimal approach in the future to combine speed and quality. And this is precisely our approach.
FAQ: Frequently asked questions about AI translations
- Are AI translations always free of charge?
No, although many tools offer free basic versions, a subscription is often required for professional features or large volumes of text. - Which languages are best translated?
European languages such as English, German, French and Spanish usually deliver the best results. - Are AI tools suitable for specialist translations?
Only to a limited extent. Without human post-editing, errors in terminology or context may occur. - How safe are AI translation tools?
Many providers promise data protection, but it is better not to enter confidential information directly into online tools. - How can I improve the quality of the translation?‘
Through clear, simple input texts and the use of tools that support terminology databases. - Are machine translations creative?
No, they can only reproduce existing patterns and are not able to work creatively. - How do AI tools differ from traditional MT systems?
AI tools use neural networks and can process context better than rule-based systems. - Which texts are unsuitable for AI translations?
Poetic, metaphorical or strongly culturally influenced texts are less suitable, as nuances are often lost. - Can AI tools translate multiple dialects of a language?
Partially. While tools such as DeepL and ChatGPT work well in standard languages, they often have difficulties with dialects. - What does the future hold for AI translations?
Improved models that can better understand specific cultural nuances and complex contexts, as well as deeper integration into workflows for organizations.
