Why AI Translation Software Is Here to Stay and How It Can Benefit You
Artificial intelligence (AI) is more popular than ever in business. Companies across all industries are now using it to deliver high-quality results in less time, including the translation industry.
Let’s take a look at what i and how it can maximize translation ROI for your brand.
Defined
What comes to mind when you hear the term “AI translation”? If machine translation (MT) works, you’re on the right track.
AI translation also encompasses large language models (LLMs) and various other machine learning, deep learning, and natural language processing (NLP) techniques.
As a result, AI has applications throughout the translation workflow, both before and after the translation phase.
How can AI help with localization?
Here are some use cases of AI in the localization process.
Pre or post-editing of text
LLMs are trained on datasets containing billions of parameters. Using NLP, they are able to identify linguistic patterns and semantic relationships. This allows them to understand and reproduce human language, which is useful for pre- and post-editing.
For example, Smartling’s GPT-enabled portal, Smartling Translate , takes into account your brand’s voice, style, and terminology to produce accurate, high-quality machine translations instantly. In our webinar on GPT and large language models , we show you how you can use Smartling Translate to:
Correct spelling, grammar, punctuation, and capitalization errors
Adjust the formality
Reword text
Using GPT to pre-edit source-language content ultimately means high-quality translated text output. But you can also use large language models to “streamline” completed translations, improving their accuracy and fluency.
Content translation
Neural machine translation ( NMT) is currently the most common form of MT, and we also see a lot of promise in using NMTs to aid in the translation process. Both use neural networks that mimic the way the human brain works.
Instead of swapping out individual words based on predefined rules, NMT engines consider context to translate words correctly. They learn belarus whatsapp number data 5 million over time, which allows them to translate more accurately. Overall, they are better than their predecessors — statistical MT models — at capturing the intended meaning of the source text.
One example of machine translation in action is Smartling’s Neural Machine Translation (NMT) Hub, which is a cloud-based MT model driven by AI. The NMT Hub uses AI to select the best MT engine for your content, resulting in the highest quality MTs in a private and secure environment.
Flow automation
AI can reduce manual workload and publishing time by automating various project management tasks, such as:
Assignment of work
Reviewing and revising content
Submitting content
Rejecting content
Smartling’s dynamic workflows use pros and cons compared to agency marketing teams AI to direct content to the right step in the process based on conditions you set. As a result, Smartling customers have been able to automate 90% or more of their efforts.
Should AI translation replace your human translators?
As you can see, AI has extensive capabilities related to translation workflows and efficiencies.
However, this doesn’t mean you should stop working with professional translators.
In the first episode of our LanguageAI Reality Series , Andrew Batwash, our Associate Director of Language Services, had this to say:
You can design AI systems to have effective and meaningful human intervention. What we want to do as service providers in the language job data services space is really think about where the human comes into the human-in-the-loop system. How do they add value? And are they in the right place at the right time to deliver the outcome that the client is looking for?”
“A lot of the talk is that we’re taking the work away from translators and moving into this fully automated world. That’s not really what it is. It’s going to be something more in between. These systems, in many ways, will help people be more productive.”
In part, this productivity advantage stems from progressive advances in the functionality and reliability of AI tools used for translation work.
How reliable is AI translation?
The higher the quality of the training data (and user input), the more reliable the AI.
The opposite is also true. This is why custom MT engines are so valuable: they are trained in your company’s specific language and domain through their translation memory and glossaries. As a result, they can produce accurate, on-brand translations.
On the other hand, this is also why some large language models struggle with content translation . Many have limited training data in target languages other than English, making it harder to generate highly accurate and culturally relevant translations.
The situation is similar to AI used for process automation, which learns from patterns in training data. Whatever patterns you define, the AI will follow, for better or worse.
Obviously, your opinion is also important.
Give an LLM a poorly written prompt and it will have a harder time getting good translation results. Give an MT engine low-quality content and the translated version will likely have some of the same quality issues.
How AI translation software has developed in recent years
AI, in general, is a rapidly developing and changing technology that has had a major impact on the quality of translation software in recent years.
Important improvements and developments include:
The introduction of the Transformer architecture in 2017, which improved the ability of models to understand context and enabled Google’s BERT and OpenAI’s GPT
The widespread use of pre-trained language models such as GPT-3 and GPT-4
The use of attention mechanisms, part of the Transformer architecture, which allowed models to focus on specific components of an input sequence when generating an output sequence and improved how models handle long sentences
The introduction of multilingual models, which allowed a single model to handle multiple languages and created faster and more cost-effective translation software
How an AI translator differs from a human translator
Why leverage artificial intelligence instead of following the traditional human-led translation process?
Speed
On any given day, your to-do list might include most or all of the following:
Outsourcing suppliers and verifying their translation services
Communicating with stakeholders, linguists and translation providers, subject matter experts and others
Preparing content for translation
Create translation tasks and assign the appropriate people at various stages of the process
Track the status of multiple translation jobs
Overseeing quality assurance (QA) and addressing issues that arise
Analyze translation reports and identify bottlenecks or optimization opportunities
All of this can be time-consuming when done manually, and you probably have other responsibilities too! Incorporating more AI into your processes is key to freeing up your time to focus on the most impactful activities.
Cost
MT costs a fraction of the price of human translation.
The latter typically costs $0.15 to $0.30 per word, but MT usually costs just $0.000010 per character. That means cost savings of tens or even hundreds of thousands of dollars per year.
You can then reinvest this money into high-priority language translation projects or use it to support other business initiatives.
Scalability
The time and cost savings associated with AI translation solutions also mean that these solutions are more scalable. You can expand into new markets and translate more content faster. And you can reinvest the money saved into greater localization efforts.
Quality
You might think that professional translators have the biggest advantage because they can capture emotions, consider context, and apply local and cultural knowledge to translations.
But incorporating AI into your processes can also contribute to quality.
Automated quality checks can bring to your attention issues that need to be fixed. Quality estimation algorithms can alert you to translations that need to be reviewed or revised.