Learning languages has been one of the highest scholarly pursuits since the dawn of time. It allowed scholars to study materials and sources in different languages. It also opened up an entirely new culture to one’s research and interpretation. It enriched one’s worldview as a whole.
Seeing how important this is, countless volumes were written about different methodologies of learning a language. Nowadays, in the hyperconnected world that we live in, these language-learning methods might be more relevant than ever.
While knowing a new language was always important, never before did it have so much practical use. Today, you can download materials in your target language, access their section of the internet, and effortlessly get in touch with the target language’s native speakers. All of this was made possible through present-day technology.
It doesn’t stop there. You see, this technology can also be leveraged in order to help one adopt a new language more efficiently. Through language-learning apps, translation apps, online courses, simulations, etc., one’s ability to adopt a new language has been drastically enhanced. With all of these factors in mind, here’s what the future of language learning might hold.
Better understanding of the learning process and learning difficulties
The truth is that there was never before more data available than it is today. This means that a capable AI-backed algorithm has access to countless examples of language learners (of all proficiency levels). This makes any conclusions made through analysis more relevant and consequential.
There are a couple of methods that were always known to facilitate the language learning process. Nowadays, with the proper leverage of technology, this can be drastically improved.
For instance, talking to native speakers and being in an opportunity where the use of the target language is necessary were difficult to achieve. With the widespread use of the internet, this is no longer so.
Second, having intrinsic motivation and objectives for learning the target language was always key to one’s success when learning the language. Never before was it more likely for one to relocate, start an overseas business, or find a long-distance partner. This covers the matter of motivation.
Being exposed to language in various formats was also a key bottleneck. Sure, you had your grammar book, but this didn’t emulate a real-life situation.
Even if you had a recording of a few spoken phrases, they were pronounced clinically accurate, without odd pronunciations, common grammar errors, or being uttered by people with speech impediments.
While this may sound like a good thing, it did nothing to prepare you for a realistic scenario. Nowadays, the availability of different materials (even YouTube videos in the target language) can diversify your learning experience.
Machine Learning and NLP
For a while now, there have been extensive efforts to teach human language to an algorithm. In the past several years, we’ve all noticed that AI has a much easier time grasping previously abstract concepts like context (in some of the most advanced cases, even complex figures of speech). All of this was only possible through the help of semantic AI technology, which helps the program understand the meaning of the word, not just analyze its etymology.
Another massive stride was made in the field of voice recognition. Now, the algorithm can recognize sounds far more accurately and distinguish words based on the context, one’s geographical location, etc.
A crucial part of machine learning in terms of language learning is also referred to as Natural Language Processing (NLP). On its own, a natural language is a form of data. However, in its nature, it greatly differs from data in a traditional sense.
Moreover, in a digital environment, the written word alone comes in so many different formats. Menus are differently structured from web pages; social media posts are differently worded from emails. All of this adds to the complexity of the context, seeing as how now you have one more factor to count into consideration.
Language Learning Apps
The next thing you need to understand is the importance of language learning apps. The reason why these apps are so great is that it provides the language learner with a lesson or a learning session on-demand. It’s like having a private tutor wherever you go.
One of the biggest problems with traditional learning was the fact that there was a presumption that you won’t have a dictionary on you in a real-life scenario where the target language is required. This is no longer the case. You can simply retake an app-based lesson or consult your in-app vocabulary on spot.
This gives a completely different approach to the situation at hand. Even professional translators are heavily dependent on translation apps, so why would ordinary language users be held to different standards?
The results of these apps alone are quite staggering. Namely, those who studied just six hours in the app increased at least one sublevel (almost 69% of them).
Moreover, 75% of those who studied for at least 15 hours improved, as well. Of course, you have the full freedom of spreading your sessions in whatever way you see fit (or taking it all in one go if your phone’s battery allows it).
In terms of cost, hiring a private language tutor for 15 one-hour classes would be quite expensive. These apps, however, either come completely free of charge or have a low basic monthly fee/subscription.
To further demonstrate the rising popularity of this industry, it’s important to mention that Duolingo (the biggest name in the market) went public on the NASDAQ in 2021.
Enhancing Multilingual Sentence (EMU) Embedding
This concept is one of the key postulates of semantics. Sometimes, you’re faced with a decision whether to keep the meaning of the phrase (semantics) or stay true to the form. This is especially troublesome when it comes to translating idioms.
This is where EMU can be incredibly useful. You see, the semantic classifier (one of the EMU components) can identify ground-truth labels and assign broader context based on them. This allows it to predict the language used in any given context.
The other two elements of EMU are a multilingual encoder (the only way this method could work across multiple languages) and a language discriminator. The language discriminator is simply there to distinguish between these two languages.
During the formative stages of the platform, efforts were made to try and “confuse” the discriminator as much as possible. The end result is a much higher resilience of the platform to such tactics.
As an end result, EMU’s multilingualism has reached its pinnacle when it comes to interpreting, translating, and distinguishing between sentences that are not exact parallels.
In the end, the ability of machines to understand human language in a way that’s similar to how humans understand it could be a major breakthrough.
A platform of this kind would be able to offer on-demand information on the semantics of any word, phrase, sentence, or text. As such, it would solve the miscommunication and translation problem right away.
Moreover, the fact that we’re living in a hyperconnected world means that there has never before been such an incentive to learn other languages. Sure, English is the lingua franca of the internet, but there are still huge chunks of the digital world that are in various other languages. Learning new languages would unlock these (currently obscure) parts to new users.