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Want better AI translations? Start here. Accurate translations are essential for global communication and avoiding costly errors. Here’s how you can improve AI translation accuracy:

• Write clear, simple text: Avoid complex sentences, slang, or idioms.
• Customize settings: Adjust tone, style, and regional preferences.
• Use post-editing: Review and refine translations for better quality.
• Leverage translation memories: Reuse consistent, approved translations.
• Provide feedback: Help AI learn and improve over time.

These strategies combine human oversight with AI tools for faster, more precise results. Let’s dive into the details.

How Can You Use DeepL Effectively? 5 Practical Tips for Accurate Translations

1. Improve Your Input Text Quality

For AI translation tools to deliver accurate results, the process begins with clear, error-free text. Studies reveal that over 75% of translation professionals agree that AI greatly enhances evaluation accuracy, but the quality of your original text plays a major role in how well these tools perform.

"The quality of the input text can have a significant impact on MT quality. Errors or inconsistencies in the source text can result in mistranslations or other errors in the output text. Additionally, the formatting of the input text, such as punctuation and capitalization, can also impact the quality of the translation." – MotionPoint

Think of your source text as the blueprint for translation. Any errors, ambiguities, or inconsistencies in your original content will only be amplified during the translation process. By ensuring your text is polished and precise, you’re setting the stage for accurate and reliable translations.

1.1 Write Clear and Simple Sentences

Overly complex sentences are a common stumbling block for AI translation systems. Complicated structures and ambiguous syntax can lead to multiple interpretations, and without proper context, the system may struggle to identify the correct meaning.

Simplify your writing by breaking down long or complicated sentences into shorter, more straightforward ones. For example, instead of: "Our team conducted a comprehensive analysis over three months, involving extensive research and data collection from multiple sources, which revealed significant insights", try: "Our team conducted a three-month analysis. Extensive research revealed significant insights."

It’s also a good idea to avoid idioms, slang, and culturally specific references, as these often don’t translate well. Phrases like "it’s raining cats and dogs" or "break a leg" may confuse AI systems that interpret language literally.

1.2 Use Consistent Terminology and Formatting

Consistency matters, especially for global organizations. Inconsistent word choices, formatting, or punctuation can lead to misunderstandings and even damage your brand’s reputation.

Stick to uniform terminology throughout your text. For instance, if you use "customer support representative" in one section, don’t switch to "customer service agent" elsewhere. Consistent capitalization, punctuation, and spacing also help AI tools better understand your content.

For businesses, managing terminology effectively ensures that all translated materials align with your organization’s standards.

1.3 Proofread Before Translating

Proofreading is essential before sending text for translation. Mistakes in your source text don’t disappear during translation – they can actually create new problems in the target language.

To catch errors, review your text for typos, grammar mistakes, and unclear phrases. Reading your work aloud or asking someone else to review it can help identify issues you might have missed. Taking a break between writing and proofreading can also give you a fresh perspective.

2. Configure Settings and Customization Options

AI translation tools often come with a range of customization features that can significantly improve the quality of translations. By tweaking settings like tone, style, and regional preferences, you can turn basic translations into polished, audience-friendly content. Many modern platforms even allow you to align translations with your brand voice and specific audience needs, ensuring the output feels tailored and professional.

2.1 Set Language Tone and Style

Adjusting tone and style settings can make translations far more precise and effective. These preferences help AI tools go beyond literal translations, capturing the emotional nuance and voice of your original text.

For instance, the choice between formal and informal language can make a big difference. Take the sentence, "You can access your account settings from the settings widget." In Spanish, a formal translation might read, "Usted puede acceder a la configuración de su cuenta desde el widget de configuración", while an informal one would be "Puedes acceder a tu configuración de cuenta desde el widget de configuración".

Tatiana Ryabinina, a localization manager at Grow-thru, highlights the value of well-designed style guides for consistent and brand-appropriate translations:

"What helps in my experience are guidelines that are actually created to put translators in a position to make copy decisions. Most style guides I have seen have do’s and don’ts, but no explanation of the reasoning behind them".

Rule-based style guides can be particularly effective when paired with AI tools. Ella Brand from Lattice describes how these guides enhance efficiency:

"I can just tell the style guide to ignore the pronouns in sentences. And I can do that across 100,000 strings".

Platforms like BeLikeNative simplify this process further, enabling users to set language, tone, and style preferences directly within the interface. Once configured, these settings can be applied seamlessly across various platforms, whether you’re working in WhatsApp Web, Google Docs, or other tools.

2.2 Configure Regional Language Settings

Regional settings are about more than just selecting a language – they ensure your translations align with local conventions for dates, currencies, measurements, and cultural references. For example, in the U.S., this means using the MM/DD/YYYY date format, dollars ($) for currency, and imperial units like feet, pounds, and Fahrenheit. Such details make your content feel natural and relatable to local audiences.

To get started, identify the specific language and regional requirements for your project. Consider aspects like the languages you need, whether real-time translation is necessary, and which features are most critical for your goals. This upfront planning helps you configure tools effectively and avoid common mistakes.

Localization glossaries are another key element for maintaining consistency across regional variations. These glossaries allow you to upload industry-specific terms and their approved translations, ensuring the AI tool uses the correct terminology every time. Translation memory databases can further improve accuracy by storing previously approved translations, enabling the AI to recognize similar contexts and reduce errors.

Even with all settings in place, testing with your target audience is essential. Local experts can identify cultural nuances and preferences that AI might overlook. For web content, using hreflang tags ensures search engines display the correct language versions to users in different regions.

3. Apply Post-Editing Techniques

Once you’ve set up quality inputs and customized settings, the next step is post-editing, which ensures the accuracy and natural flow of your translations. Even the most advanced AI translations benefit from human oversight. Post-editing fine-tunes the machine’s output, catching errors and polishing the text for clarity and precision. Depending on your needs, you can opt for light post-editing to focus on speed and basic clarity or full post-editing to refine tone, terminology, and nuances. Defining clear quality standards upfront is crucial – whether you’re aiming for basic understanding or content ready for publication. Below are the key steps to enhance your translations.

3.1 Review Translations in Multiple Stages

A single review pass often isn’t enough to spot every issue. AI translation engines rely on semantic probabilities, which can sometimes lead to unexpected or unusual errors. To ensure thorough quality control, break the review process into multiple stages. Start with a terminology and accuracy check, focusing on technical terms, proper nouns, and critical facts. Next, review grammar and syntax. Finally, assess the overall flow and readability by reading the text independently from the source material.

For example, ActiveCampaign improved its localization process in 2024 by combining automated QA tools, translation memory, and glossaries. This approach cut costs by 16% while boosting website traffic by 73%. Training your review team to spot common AI translation errors can further streamline the process. Automated QA tools are especially useful for catching basic issues like spelling mistakes, grammar problems, and terminology inconsistencies before human reviewers step in.

Rachel Ferris, Chief of Customer Success at Acclaro, highlights the importance of preparation:

"The LSP should collaborate with you to create detailed glossaries and style guides to ensure consistency of niche terminology, style, voice, and incorporate target audience information".

3.2 Use Back-Translation for Quality Checks

Back-translation is a powerful tool for identifying errors and inconsistencies. This process involves translating the AI-generated text back into the original language and comparing it with the source material to uncover any loss or distortion of meaning. Significant differences in the back-translated version often point to potential issues. This method is especially effective for technical, legal, or marketing content where precision is critical.

Rather than treating back-translation as a separate step, integrate it into your regular review workflow. Use it selectively for high-stakes content or areas prone to errors. To avoid missing issues, consider having a different person or tool handle the back-translation than the one used for the initial translation.

Building a feedback loop is essential for continuously improving your post-editing process. Track recurring errors and use this data to adjust your AI tool settings, improve the quality of your input text, or focus your review efforts on specific problem areas. This data-driven approach not only improves results over time but also reduces the overall effort needed for post-editing.

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4. Build and Update Translation Memories

Translation memory (TM) systems are like a library of previously translated text segments, stored in language pairs. These systems ensure consistent use of translations and make it easier to reuse content efficiently. When new content comes in, the TM system searches for matching translations, cutting down on post-editing work. This capability is a game-changer for tailoring TMs to fit the specific needs of your industry.

TMs don’t just save time – they also save money. According to Smartling‘s 2024 State of Translation Report, translation volumes have jumped by 30% year over year, highlighting how critical efficient TM systems have become. Modern AI-powered TM tools take it a step further by using machine learning and natural language processing to understand context, moving beyond simple word-for-word matching to deliver more accurate results.

Smartling’s 2024 State of Translation Report states, "Translation memory automatically recognizes and reuses previously translated words, reducing the translation workload while providing cost savings and increased efficiency for clients".

4.1 Create Domain-Specific Databases

Generic TMs are helpful, but domain-specific ones are where the real magic happens. Tailored translation memories for specific industries – like legal, medical, or marketing – ensure that the unique terminology, tone, and context of your field are accurately captured.

Start by gathering high-quality translations from your industry. These specialized TMs are proven to boost accuracy in niche applications. For example, industries that rely on precise language, such as technical or compliance-heavy fields, can see up to a 50% improvement in accuracy when AI-driven TM tools are configured with domain-specific content.

To get started, standardize your terminology early on. A translation glossary that clearly defines how specific terms should be translated can prevent inconsistencies that might confuse readers or damage your brand’s image. Segment your content into logical units to allow for more precise matches and better translation suggestions. Pay special attention to industry-specific phrases, regulatory requirements, and branded language that must remain consistent across translations. And don’t forget – your TM will stay relevant only if you update it regularly.

4.2 Update Translation Memories Regularly

Translation memories are not a "set it and forget it" tool – they need regular updates to remain effective. Without proper maintenance, your TM could end up cluttered with outdated, duplicate, or incorrect entries, which can hurt translation quality.

Make it a habit to clean up your TM periodically. Remove outdated entries, merge duplicates, and fix any formatting issues. Segments with questionable quality should also be removed, as they can negatively affect automated translation results. For example, ActiveCampaign has successfully managed its TMs by organizing them by content type and updating them regularly. This approach has saved the company 16% on translation costs and sped up workflows, allowing localized campaigns to launch in just two days.

It’s a good idea to assign someone to oversee TM management. This person can ensure that the right language pairs are used, verify that uploaded content matches the intended context, and monitor which entries need review or removal based on feedback from translators and usage patterns.

Create a feedback loop to keep improving your TM. When human editors make corrections to AI-generated translations, those refined versions should be added back to the TM. This helps the system learn from past mistakes and offer better suggestions in the future. Tools like MemoQ’s predictive matching, which reduces post-editing work by 35%, and Trados Studio’s adaptive models, which cut human editing time by 40%, show how continuous updates can significantly improve efficiency.

Lastly, lock high-quality segments to prevent accidental changes and ensure consistency. Use automated quality checks and conduct regular linguistic evaluations to catch errors before they spread. Well-maintained TMs not only improve translation quality but also enhance the performance of AI tools over time, creating a cycle of continuous improvement.

5. Improve AI Models with Feedback

High-quality texts and thorough post-editing lay the foundation for precise translations, but feedback is the key to pushing AI models to the next level. By integrating feedback loops, AI translation systems can continuously learn and improve. For instance, A/B testing with user input has been shown to boost task-specific accuracy by over 20% within just six months. Research supports that machine learning-equipped AI systems thrive on user feedback and past experiences, creating a cycle of ongoing improvement.

5.1 Set Up Feedback Systems

Simple feedback systems, like rating scales and comment boxes, make it easy for users to share their thoughts on translation quality. A five-star rating system or thumbs-up/thumbs-down buttons can provide quick evaluations, while comment boxes allow users to highlight specific issues. This input is invaluable for refining glossaries, improving translation models, and fine-tuning style guides.

Take LILT‘s Contextual AI Engine as an example – it uses expert feedback to retrain instantly, improving both accuracy and consistency. Language experts play a critical role here, as they can catch subtle errors and nuances that automated systems may overlook. Asking targeted questions like, “Does this translation retain the original tone?” or “Are technical terms translated correctly?” can help pinpoint areas needing improvement.

5.2 Track Translation Quality Metrics

Metrics are essential for measuring how well feedback enhances AI translations. While traditional metrics like BLEU scores are popular, they don’t always capture cultural nuances or context completely. Combining automated metrics with human reviews offers a more balanced evaluation.

Recent studies highlight the impact of tracking quality metrics. For example, machine translation for technical documents has reached an 89% accuracy rate, and APET metrics reveal a 50% reduction in translation time when using AI in 2024. Some key metrics to monitor include:

An effective feedback loop ensures that corrections made by human editors are fed back into the system, improving future translations. Research shows that integrating metrics at various stages of the translation process aligns AI outputs more closely with human judgment and minimizes major errors.

Regularly updating AI models with insights from quality metrics, evolving language trends, and dialect variations ensures continued progress. Engaging native speakers and linguistic experts offers additional insights that further enhance translation accuracy over time.

Platforms like BeLikeNative streamline the feedback process by embedding it directly into your workflow. Supporting over 80 languages and offering customizable tone and style settings, it allows you to collect detailed feedback on how well translations match your intended voice and context. This data becomes the foundation for refining future projects and achieving better results.

Conclusion: Getting Better AI Translation Results

Achieving accurate AI translations isn’t just about relying on technology – it’s about using smart strategies and effective tools to create a smooth workflow. The five tips in this guide can reshape how you tackle translation projects, whether you’re working on business communications, technical documents, or creative content.

The key lies in combining high-quality input with proper customization. By fine-tuning tone, style, and regional preferences, you lay the groundwork for more precise results.

"Machine translation post-editing (MTPE) combines the accuracy of human translators with the speed of machine translation".

This hybrid approach offers the best of both worlds: speed and precision. Adding clear editorial guidelines and quality checks further enhances the outcome.

Post-editing and regular feedback are essential to refining AI translations. By updating translation memories and incorporating user feedback, you help AI tools adapt to changing language trends and reduce errors over time.

Specialized tools can also make a big difference. For example, BeLikeNative supports over 80 languages and allows tone and style customization. Users have reported up to a 66% boost in productivity, thanks to the tool’s ability to learn from corrections and preferences, creating a tailored and ever-improving translation experience.

Starting with clear goals and the right approach is crucial for each project. As Adam Bittlingmayer, CEO of ModelFront, wisely notes:

"It’s better to let the professional human translators do their work".

This doesn’t mean abandoning AI – it means using it strategically while ensuring human oversight where it truly counts.

FAQs

How can I make sure my AI translation tool captures the tone and emotion of my original text?

How to Help Your AI Translation Tool Maintain Tone and Emotion

To ensure your AI translation tool captures the tone and emotion of your original text, start by crafting input that’s clear and rich in context. Steer clear of ambiguous phrases or idiomatic expressions that might not translate well across different languages. Including extra details, like who the audience is or the purpose of the text, can help the AI better preserve emotional nuance.

Once the translation is generated, take the time to review and refine it. Post-editing is key to fine-tuning elements like tone, style, and emotional depth that the AI might not fully grasp. Tools such as BeLikeNative can be incredibly useful, offering features like rephrasing and tone adjustments to make sure your message remains consistent and emotionally aligned across languages.

How can I give effective feedback to improve AI translation accuracy?

Improving the accuracy of AI translations starts with providing clear, actionable feedback. Begin by carefully reviewing the AI-generated translation and manually correcting any errors or awkward phrasing. This process helps the system learn from its mistakes and gradually improve its output.

Another essential step is leveraging a terminology database. This ensures translations remain consistent and precise, particularly in specialized areas like legal or technical content, where accuracy is non-negotiable. Pairing this with regular quality checks and feedback loops allows the AI to fine-tune its performance based on actual user interactions and needs.

When human oversight is combined with structured feedback, the result is a steady improvement in the accuracy and dependability of AI translations.

What are translation memories, and how can they improve my business’s translation process?

Translation memories (TMs) are like organized libraries for translations. They store previously translated text segments – like sentences or phrases – so they can be reused in future projects. When new content needs translating, the system checks it against the database and suggests matches, helping to keep the language and tone consistent.

For businesses, TMs offer big advantages. They ensure a unified brand voice across all documents, cut down on translation costs by reusing past work, and speed up the entire process. Plus, the more you rely on TMs, the smoother and more cost-efficient your translations become over time.

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