Intro
DeepL leverages deep convolutional neural networks (CNNs) to generate high-quality translations. While CNNs are traditionally used in image recognition, DeepL has adapted this technology to focus on linguistic relationships and patterns.
The Five Layers of DeepL’s Neural Network:
- Convolutional Layer: Scans input text for recognizable patterns.
- Activation Function: Identifies which patterns are significant.
- Pooling Layer: Condenses data to highlight key patterns.
- Fully Connected Layer: Links patterns to form meaningful translations.
- Decoder Layer: Constructs the final translated output.
DeepL is trained using bilingual data from Linguee, combined with an advanced web crawler that identifies new translations. This approach ensures continuous improvement and high accuracy.
How Accurate Is DeepL Translator?
DeepL consistently delivers exceptional accuracy across various linguistic categories. According to a study conducted in 2020, DeepL achieves an overall accuracy rate of 89%, outperforming its closest competitors in most areas.
Accuracy Comparison: DeepL vs. Google Translate
| Category | Google Translate | DeepL |
|---|---|---|
| Ambiguity Handling | 64.5% | 74.4% |
| False Friends | 69.4% | 83.3% |
| Verb Valency | 57.4% | 91.5% |
| Non-Verbal Agreement | 90.2% | 92.7% |
| Subordination | 74.7% | 72.5% |
| Verb Tense/Aspect/Mood | 69.0% | 71.6% |
DeepL vs. Human Translation
While DeepL is highly accurate, it falls short compared to human translations. Issues such as handling intertextual references, degrees of politeness, and cultural nuances remain areas where human translators excel.
DeepL vs. Google Translate
A detailed comparison shows that DeepL outperforms Google Translate in most linguistic categories. The table below highlights how both tools handle idioms:
| Category | Google Translate | DeepL |
|---|---|---|
| Continuous Form | 86% | 94% |
| Discontinuous Form | 71% | 81% |
| Combined Form | 79% | 88% |
| Global Results | 70% | 78% |
DeepL excels in handling idioms and collocations but struggles with lexical ambiguity, as demonstrated below:
| Scenario | Google Translate | DeepL |
|---|---|---|
| Lexical Ambiguity | Better Performance | Weaker Performance |
| Collocations | Mistranslates “den Tisch decken” | Accurately translates |
Improving DeepL’s Translation Accuracy
To maximize DeepL’s translation accuracy:
- Use Post-Editing: Have human translators review and refine machine translations.
- Leverage Localization Platforms: Platforms like Ranktracker's tools help streamline translation workflows for websites and documents.
When Should You Use DeepL?
DeepL is suitable for:
- Professional Translations: Marketing, legal, and medical documents.
- High-Quality Content: Websites, publications, and technical guides.
- Large-Scale Projects: Handles high volumes without compromising quality.
- Multi-Language Translations: Ideal for global businesses with multilingual needs.
Conclusion
DeepL is one of the most accurate machine translation tools available today, outperforming competitors like Google Translate in key areas. While it cannot fully replace human translators, its performance makes it invaluable for businesses seeking efficient and reliable translations.
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