Recent years have witnessed tremendous advances in artificial intelligence due to advancements in machine learning and neural networks. AI now forms part of most software programs, ranging from autocorrect to music composition; translation technology is one of the primary drivers of innovation within this space. Google and Microsoft are investing billions into translation systems as they race for this $40 billion market.
Despite the progress, several challenges persist.
Algorithmic Bias Can Be Difficult to Address
Algorithmic bias is one of the biggest challenges to AI, yet also one of the hardest problems to address. Algorithms often become biased as a result of who builds or trains them and by the social environment in which they’re created.
Imagine an algorithm that predicts someone’s job based on an image of their facial features. Such an algorithm would need to take into account certain groups, such as genders, races, or ages, who are more likely to hold certain jobs than others.
If most programmers are male and of a certain race, the algorithm will likely reflect this data.
One of the more frequent sources of bias in AI translation technology is gender. For example, algorithms may assume doctors and nurses are male, while English does not use gendered pronouns exclusively; Google has attempted to address this by translating using both genders when relevant, adding another layer of complexity to AI translation processes.
Machine translation can also be biased in other ways, and not all of them are moral issues. For instance, algorithms often have a bias toward shorter translations, which can result in translations that are too brief. In some cases, they may even influence human translators by affecting the materials they are quality-checking.
How can technology developed by humans, who are susceptible to bias, ever be completely free of it?
The Word Accuracy Has Improved, But Not Much More
In recent years, data science has made significant advances, but much of this is due to faster, more powerful computers that can process more data in less time. AI advancements often stem from doing more faster, allowing algorithms to compare larger data sets and identify more patterns.
This has led to improved accuracy in word translation through AI-based language translation. I believe that, in the future, this technology will reach a level of accuracy that can be relied upon for professional translation.
However, AI translation technology still faces many language problems, and word accuracy alone isn’t the solution.
Humans don’t communicate using isolated words alone. Words that are used together and have similar meanings can combine to form sentences, paragraphs, or entire works of writing. We use metaphors, satire, and irony to add color to our language, and we rely on tone and articulation for emphasis.
Now, try explaining irony and how it’s similar to but also different from sarcasm or satire. Next, try turning that explanation into an algorithmic dataset.
Let’s forget about translation for a moment. AI struggles to detect these features in language (which can be difficult enough for human translators). If AI algorithms become smart enough to accurately translate human speech, including accents, dialects, and speech impediments, into text, we might be able to address the larger challenges of AI translation.
Is AI-Based Language Translation a Part of Professional Language Services?
AI translation plays an important role in professional language services, and this role will continue to grow. However, this doesn’t mean AI technology will replace professional translators. Instead, AI will assist humans by automating more of the translation process as it improves.
Professional translators can deliver 100% accuracy faster if more tasks are automated and more reliably executed. The cost and turnaround time of translating content will be further reduced, even for complex projects.
These time and cost savings will allow businesses to achieve more with fewer resources as they rely more on translated content. This type of technology will make translation more affordable for smaller companies that were previously unable to access such services.
Conclusion
While AI translation technology has made remarkable strides, it still faces significant challenges, including algorithmic bias and limitations in understanding complex human language features. However, AI is not likely to replace human translators entirely. Instead, it will serve as a valuable tool that enhances the speed and affordability of professional translation services. As the technology continues to improve, it will likely become an even more integral part of the language services industry, helping businesses communicate globally while reducing costs and increasing efficiency.
