Artificial intelligence has progressed a lot in recent years thanks to developments in machine learning, neural networks and data science. It now seems every form of software imaginable has AI alternatives that promise to automate human thinking and decision-making – everything from autocorrect to music composition. Translation technology is no different and this is actually one of the driving forces for some of the strongest innovations in AI development. Tech giants like Google and Microsoft invest billions into their respective translation technology systems and are leading the lucrative race in this £40 billion + market.
However, there are some persistent challenges which are buffering progress.
Algorithmic bias is one of the most talked-about problems within AI technology and it is proving to be one of the most difficult to overcome. The stark reality is that algorithmic bias is inherited from the humans that build and train them as well as the social environment we live in.
Imagine an algorithm designed to predict the job of someone using only an image of their face. We know that jobs are disproportionately filled by genders, races, ages and other groups of people, which will be adopted by the algorithm being used.
If the majority of programmers are white males, then the algorithm is going to develop a basis that reflects this type of data.
With AI translation technology specifically, one of the most prevalent forms of bias relates to gender, which can be present in assuming nurses are female and doctors are male. This issue is made more complex by the fact that most languages rely less on gender pronouns than English. Google has tried to solve this by providing translations using both genders where relevant but this adds further complexity (algorithmic decision-making) to the AI language translation process.
There are various other forms of bias that machine translation is susceptible to and they are not always moral issues either. Algorithms often develop a bias towards length that results in translation being shorter than they should be or even cause bias in the human translators who are influenced by the material they quality check.
The big question is: how can translation technology (which was developed by humans, who are often susceptible to bias), ever be free from bias?
Data science has progressed enormously in recent years, but most of this progress is the result of faster and more powerful computing technology that is capable of crunching more numbers in a shorter timeframe. All advances in AI revolve around doing more and faster in order to enable algorithms to compare more datasets, which results in more patterns being spotted.
This has resulted in greater word accuracy in AI language translation, and there is a fair reason to be optimistic that this translation technology can achieve a level of accuracy in future that will be reliable enough for professional translation use.
However, word accuracy doesn’t even begin to solve AI translation technology’s language problems.
Humans don’t communicate with isolated words alone though. When words are paired with other words that affect each other’s meaning; they construct sentences, paragraphs and complete entire works of speech or writing. We provide context; imply meaning; use tone and articulation for emphasis; make comparatives; employ metaphors; and, add colour with satire, irony and a wealth of linguistic characteristics that cannot be defined by rules.
First, try explaining the concept of irony and how it is similar but also different from sarcasm and satire respectively – now try to turn this explanation into a dataset that algorithms can apply to human language.
Forget translation for a moment, AI is not even capable of detecting these characteristics in language, let alone translating them (this can be challenging enough for expert human translators to deal with). If AI algorithms ever get smart enough to accurately transcribe human speech into text (including languages, accents, dialects, speech impediments and all the other characteristics the human brain naturally computes), we might be able to address the bigger challenges of AI translation technology.
AI language translation absolutely has a place in professional language services and its role will only going to grow with time. This doesn’t mean that AI translation technology will replace professional translators, but it will help humans to automate more of the translation process as it continues to improve.
The more of this process we can automate (and the more reliably we can automate it), the faster professional translators will be able to deliver 100% accuracy. This will further reduce the expense and turnaround times of translating content, even for the most complex projects.
As companies increasingly rely on translated content for a growing number of purposes, these time and cost savings will help businesses achieve bigger goals with the same resources. At the same time, this type of translation technology will and currently makes translation services more accessible to smaller businesses that may have previously been out of reach – and the pool of companies expanding into international markets will continue to increase.
If you want to find out more about how AI translation technology can help your business achieve bigger things, contact the MT team here at translate plus by filling out the form on our contact page.