ULG's Language Services Blog

Six Myths About AI Translation

From faster turnaround times to increased content, the benefits of AI translation are real and compelling. Google Translate processes at least 146 billion words a day.  According to Nimdzi Insights, Neural Machine Translation (NMT) can translate three times more than all of the professional translators in the world could translate in a month. AI translation also helps optimize translation budgets, so organizations can make the most out of each precious dollar. 

Yet, as with any trending topic, it can be difficult to separate hype from reality. In this article, our experts will cut through the noise to help you determine if AI translation is a good fit for your business and how to integrate it effectively. 

Types of AI translation and machine translation software 

There are two kinds of AI translation out there: neural machine translation (NMT) and large language models (LLMs). 

These have largely replaced older MT models like rule-based machine translation (RBMT) and statistical machine translation (SMT). RBMT attempts to explicitly encode all linguistic rules in the source and target language, while SMT uses statistical probability models to suggest likely translations. There are still a few specialized MT engines that use these models, but AI is the go-to in most situations.  

Neural Machine Translation (NMT) 

NMT is an AI-powered type of machine translation that uses complex machine learning algorithms, designed to mimic the way human brains process language. NMT is designed to accurately translate text from one language to another. This type of translation isn’t new; it was adopted by Google in 2016.  

There are a number of different NMT systems on the market, each providing different quality translations depending on the type of content and language pair involved. For example, many MT engines struggle with translating languages like Arabic, Ukrainian, and Korean into English, due to differences in grammar and syntax and the amount of translation data available for training. Commercially available MT engines can be highly specialized, down to the language pair and the specific industry.  

Language services providers (LSPs) can also customize NMT engines using data from specific industries, or even from a particular business for additional precision. 

Large Language Models (LLMs) 

The current AI headlines focus on LLMs, like ChatGPT. These models are fed tremendous amounts of data, and not just translation data.    

LLMs are so sophisticated that they don't just translate—they can craft content, chat back, and even spin a story from a short prompt. Thanks to these capabilities, they can potentially assist in localization tasks beyond translation, like source content optimization, multilingual content creation, and proofreading. But there’s a catch: LLM translation is still less accurate than a well-calibrated NMT engine, and it’s hard to predict ahead of time when and where it’ll be the best fit. 

As this technology develops, experts have suggested that LLMs might be able to create multilingual content directly from a brief in the source language, with linguists proofreading and copyediting the resulting output. These are all potential applications to keep an eye on, but currently the results LLMs produce aren’t consistent enough for organizations to rely on them for these purposes.  

ChatGPT and its LLM cousins like Bard (Google) and Bing chat (Microsoft) represent a leap into the future of language tech, but that doesn’t mean ChatGPT translation is the right solution for your business today.  

6 myths about AI translation services   

 NMT has a proven track record and LLMs are promising, but implementing AI translation isn’t simple.  There’s much more to MT than simply hitting a button and getting a translation. 

When it comes to AI translation, here are six common misconceptions our team is noticing, and the realities behind them.  

Myth 1: Google Translate (or another generic translation engine) is all you need.  

Fact: Google Translate, Bing Translator, and other similar tools are best suited for quick, ad hoc translations rather than professional, business-critical use. Plus, the potential for errors to go uncorrected is simply too high. Customized MT solutions can often achieve better results.  

There isn’t one best MT engine out there. Different MT engines provide more accurate translations of certain languages, which means that some MT engines will be a better fit for your business and do a more accurate job of translating your content than others. The key is to find the best machine translation services for you.  

Myth 2: You don’t really need humans.   

Fact: While AI and machine translation tools are impressive, they're not perfect. To get good results, you still need human experts to guide the translation process and post-edit the output.  

AI-powered machine translation tools have made it possible to translate more content than ever before while maximizing budgets. Yet they still struggle with complex sentence structures, cultural nuances, and context-specific interpretations.  

Even large language models like ChatGPT perform best when complemented by human expertise, ensuring cultural accuracy, brand consistency, and overall quality in content.  

To get the most out of MT, think “collaboration” not “replacement.” It’s not about human translation versus machine translation—the human touch is irreplaceable.  

Myth 3: Machine translation is free.  

Fact: There are free online tools like Google Translate available, but they aren't tailored for business needs. They’re designed for everyday consumers, not organizations that need to wrestle with issues like data security, brand reputation, or liability. 

Customized AI language translation services can generate more accurate results, but for critical content, post-editing is still needed to ensure quality. Using raw MT output in any context in which accuracy is critical can cause confusion for your customers, create a cascade of new costs to correct errors, and possibly even create legal risks for your business.   

Machine translation can certainly help you optimize your translation budget, but it is not free.   Customization and maintenance of the engine bears a cost, and so does post-editing (PE). When you factor in post-editing by experts to ensure quality, the cost savings from machine translation can be up to 38 percent, not 100%..  

Myth 4: AI translation quality is high these days.  

Fact: The quality of machine translation has certainly improved in recent years. Whether it’s high enough for your business needs or not depends on a number of different factors, including the languages involved, the subject matter, and the specific tools used. Even under the best possible conditions, it's not immune to errors. 

Bias in AI translation is also a real concern. AI algorithms are trained on vast datasets. Any biases in those datasets can inadvertently get reflected in the translations the AI provides. Bias in, bias out. This can cause translations that perpetuate damaging stereotypes, or that are inaccurate and offensive.  

The only way to ensure quality results from AI translations is a robust post-editing process. For this, you need skilled linguists, able to spot errors, bias and cultural nuances and produce an error-free final product with consistent style and terminology throughout.  

Myth 5: MT engines are secure.  

Fact: AI translations can be done securely, but not on public engines like Google Translate and Bing. These public engines present serious data protection issues. The text you enter is often used by the provider to improve future translations, potentially exposing sensitive information and intellectual property. 

MT translations are safe and secure when you’re using an MT system with the appropriate safeguards, and when it’s integrated into secure workflows. A language services provider can help you choose an MT engine that meets your organization’s requirements and complies with all relevant data protection laws.  

Myth 6: AI translation can be implemented immediately.  

Fact: You can’t implement MT or AI translation overnight. To successfully integrate these tools, you need a comprehensive strategy. It's not merely about choosing the right MT system but about tailoring its usage to fit specific business needs and audiences. 

AI translation can integrate with existing workflows, tools, and content management systems. You need to ask yourself: Do you know what these new processes and systems will look like? Do you have a plan to ensure that critical content is carefully post-edited by experts in the target language? 

Is your organization prepared for AI translation? 

MT isn’t a quick fix, and it’s not a magic bullet. There are significant risks to jumping in without a clear roadmap in place. You need to know when machine translation services are viable for your business, which of many engines to implement, how to customize, and when human linguists are required. Your organization likely has different departments and scenarios where MT could be used.  It’s important to identify these scenarios and find the best integration for each. Not only do you need to choose the most appropriate tools, but you also need a comprehensive strategy to make sure these tools align with your specific business needs and goals.  

Our experts are experienced in using AI and MT to streamline workflows and maximize budgets. For example, we helped a Life Sciences company reduce translation costs by 90 percent over two years while improving worker morale and productivity. We’re here to help you navigate your AI journey, we can help you design a strategy that will guide you to success. Contact us for a consultation today!