Want to boost your global sales but struggle with the scale and complexity of translation? With AI translation and powerful LLMs now in the game, capabilities to do so are changing fast. Technological developments and advances over the past two decades have meant that machine translation (MT) is more accurate now than ever before—and it will continue to improve.

Many companies are now taking their business global at high speed and low cost with these tools, but concerns remain about translation quality and consistency. Overcoming these obstacles is all about how, when, and where you integrate AI into your localization process—carefully implementing workflows and quality control procedures that maintain efficiency while maximizing quality.

But first, we caution you to be careful about what you think you know about AI in translation: there are a lot of myths out there and some incorrect (even dangerous) thinking around how to deploy these tools.  Read on to discover how these innovative solutions can help you expand internationally without compromising on accuracy or breaking the bank in our no-nonsense guide to machine translation for business.

How AI Translation Works

There are different models of machine translation, including rule-based and statistical machine translation, but we will focus on neural machine translation (NMT) and large language models (LLMs) because they are by far the most commonly used right now.

Neural Machine Translation

Neural machine translation (NMT) uses artificial intelligence to convert text from one natural language to another. It employs machine learning and neural networks to predict the most accurate translation outcome.

Neural networks are a subset of machine learning and are at the heart of deep learning algorithms. Inspired by the human brain, they mimic the way biological neurons signal one another, making NMT more sensitive to context and nuances than earlier, cruder forms of machine translation. NMT engines are trained on massive bilingual data sets. Quality can suffer if there isn’t enough bilingual data available, so it doesn’t perform as well with low-resource languages.

One way to improve NMT quality is to train the algorithm with industry-specific data. This gives the NMT engine more context, so it can make better predictions.

Another exciting twist to NMT is adaptive machine translation, which lets translators fine-tune machine suggestions in real time. This means the system learns new terms, phrases, and your brand’s tone on the fly, boosting translation quality and efficiency.

Large Language Models (LLMs)

LLMs, such as ChatGPT-4o, bring another level of fluency and creativity. They handle rare languages more effectively than NMT engines and don’t always need a source text (though they will still need detailed instructions to generate new content for your target market). However, they can be unpredictable, with a higher risk of errors and biases. Pretrained on vast amounts of publicly available data, LLMs offer speed and cost savings but lack NMT’s domain-specific customization options. Also, because they are trained on publicly available data, they can carry over inaccuracies, biases, and stereotypes that exist there, and the industry must address this.

Knowing the strengths and quirks of NMTs and LLMs can help businesses choose the best AI translation tools and processes for smooth, scalable global operations.

Doesn’t Machine Translation Mean Lower Quality?

It used to, but not so much anymore.  If you tried to translate your company website using Google Translate back in 2006, you probably found it nearly illegible. Back then, Google was using statistical machine translation. Today, even raw machine translation (straight from the engine, no human intervention), including from Google Translate, is much more accurate thanks to the shift towards NMT and LLMs.

There are no hard and fast statistics for MT quality or accuracy because it varies greatly depending on how technical, specialized, or colloquial the source text is, on the language pair, and on the specific MT engine used. For example, in general, translating from English to Spanish is likely more accurate than translating to a less common language like Scots Gaelic. However, some translation engines will likely perform better with this language pair than others.

MT quality is also constantly in flux. As these translation engines are fed more data over time, their accuracy improves. At the same time, not all content requires top-quality translation. For instance, FAQ content can often get by with less precise translations as long as people’s questions are still being answered correctly. Marketing content, on the other hand, demands higher accuracy to maintain brand voice and impact.

One way to get the most out of machine translation is to customize the processes to your needs, with varying levels of post-editing from skilled linguists. This flexibility allows businesses to balance quality, speed, and budget effectively.

Disadvantages of AI Translation

NMT and AI translation engines are a boon to businesses that need to stretch their resources as they expand globally. But there are also pitfalls. A successful AI translation strategy hinges on avoiding these:

• Bias in AI translation: As we mentioned, LLMs are trained on vast amounts of human-generated data, carrying with it the many biases that we have yet to overcome. These biases can bleed into translations, leading to skewed and sometimes inaccurate results. For example, a female doctor could end up being referred to as a “nurse” due to outdated stereotypes, or a doctor could never be represented as female.
• Loss of cultural nuances: Cultural details and emotions can get lost, making translations feel flat and impersonal.
• Security: Free, publicly available tools like Google Translate and ChatGPT may expose sensitive data to breaches. This is another excellent reason to use a reputable language solutions provider, as they will have safeguards for data protection.
• Unpredictability: Due to unclean and uncontrolled training data, AI outputs can be unpredictable and filled with errors, omissions, and misleading information.

These challenges highlight the need for careful tool selection, stringent data handling, and human oversight to guarantee quality and security.

What is Post-Editing and When Would I Want It?

One of the best ways to overcome these disadvantages is to incorporate post-editing into your AI translation workflows. Post-editing involves human translators refining AI-generated translations. There are two levels: light and full.

Light post-editing corrects basic errors for clarity. It’s faster and cheaper, making it suitable for FAQs or simple content. However, it may miss subtle nuances and fine details, and your content may not fully align with your brand style and tone. Full post-editing polishes every detail, ensuring high-quality, accurate translations. It’s ideal for marketing materials and critical documents. While more thorough, it takes longer and costs more.

With post-editing, you get the speed of AI with the precision of human oversight. This process helps you avoid mistakes and awkward translations so that your content is both accurate and polished. By combining AI and human expertise, you achieve the best of both worlds.

AI translation is perfect for content with a short shelf life or low visibility, like FAQs, user reviews, or service content. Here, it excels at translating large volumes quickly and cost-effectively, saving time and money.

For highly nuanced content, such as corporate messaging, taglines, or advertising copy, AI alone can fall short. It can miss the subtle cultural and emotional details required to maintain brand integrity and really connect with customers in all markets. Here, human involvement is essential.

Most of your content will likely fall somewhere in between. For these cases, combining AI translation with post-editing strikes a balance. You get the efficiency of AI and the accuracy of human oversight, guaranteeing high-quality translations without compromising on turnaround times or budget.

Getting Started with AI Translation at Acclaro

At Acclaro, we bring the power of AI translation to your business with advanced engines designed to meet your needs. Here’s how we do it:

• Listening to your needs: We start by understanding your specific requirements, including language sets, content volumes, and desired quality levels.
• Content evaluation: We assess your content to determine its suitability for AI translation.
• Engine selection: We choose the best translation engine for your project and test its output to ensure it meets your standards.
• Engine customization: If needed, we customize the selected engine to better align with your content and brand voice.
• Post-editing assessment: We evaluate the level of post-editing required to achieve the perfect balance of speed and quality.

Our AI-powered translation platform, WordsOnline, is ISO18587 certified for ‘Post-Editing of Machine Translation Output.’ It combines AI’s speed and agility with the expertise of our skilled linguists, ensuring high-quality, authentic translations.

You can easily start your AI translation project with our Translate Now instant ordering service. Simply upload your files, choose from 40 languages, select your service level, and pay online. For ongoing needs, we offer a flexible subscription service through WordsOnline, providing you with translation tokens to redeem for various services.

Ready to start powering your global expansion with AI translation? Contact us to get started!