Chatbot Teaching Languages: The Contributions of AI, Deep Learning & NLP

We have seen Artificial Intelligence to transform every industry sphere to the extent possible, and this is true in the field of education as well. With every day witnessed advancements in technology reduces the possibilities of excuses for not learning non-native languages.

Today, it is no longer necessary to go to a language school or visit another country in order to initiate a conversation in another language. Language barriers have been erased by AI-powered devices which are bolstered by solid neural science and deep learning algorithms to get people learning and talking new languages.

There are about 6500 different global languages, ultimately 6500 ways to communicate around the world. And logically thinking, it won’t take time reaching a particular place rather it would take to learn the language being spoken there. Isn’t it

Today, backed up by technology, we are able to connect with the worldly population within a few taps on a smartphone, but it also becomes essential to communicate with each other and understand their culture keeping language restrictions at bay.

So now, can technology give a solution to this?
Well, yes. Artificial Intelligence (AI) gives a custodial solution.

More specifically, Natural Language Processing (NLP) and Deep Learning , robust technical frameworks (and subsets of AI).

Now the question raised is that can the solution be embedded in a Chatbot or any other Bot?

One can say that with the use of a chatbot one can practice their learning language skills more efficiently and in a distinguished way. Using a chatbot offers an array of advantages:

– Unlimited Availability
A person need not lose any hours in commuting/getting late to the learning center or missing a class due to the absence of the tutor. One can learn from anywhere, anytime.

– Cost-Effective
Certainly, it can help save you bucks as many application chatbots are made available for free.
Since practicing with a native speaker can represent an advantage in the acquisition of knowledge, a chatbot can emit different accents and tones, in some cases, in addition to the fact that for texts, it will display phrases and sentences completely adhering to the spelling rules.

We all know that chatbot is a simulation of human conversation which processes the given inputs through natural language processing. The entire message/sentence is broken down into texts of language as the entire message can’t be encrypted in one go.

Some of the recommendations are:

# Mondly

This is a British application where you can learn 30 distinct languages where their specialty lies in English. One doesn’t need to make any subscription or any payment to make a head start; however, it has a premium version that includes access to all the languages.

In addition to chat, Mondly has a version in which it uses Virtual Reality for the user to establish conversations with various characters of different nationalities, depending on the language.

# Duolingo Chatbot
This is one of the most popular applications for learning languages. In addition to its games and activities, it launched a new chatbot functionality that, through various characters, allows you to have conversations in different languages.

This modality is available through its traditional platform, both in-app and on the web. As the characters that are available in the chatbot are supposedly native to various countries. The voice memos are heard in an original accent to improve pronunciation.

[Prefer Reading: “Deep Learning in the Digital Age: Top Applications of the Real-Time World.” ]

Chatbots can simulate conversations in different languages and the translation is supported by Deep Learning algorithms that are inspired by human neural networks and can be further branched with the principles of machine learning.

Sequence to sequence learning is used to solve distinct kinds of translation problems where it concerns language learning or its related to image translation. The estimate of how correct is something is deduced by how similar it is to the training data.

Here are a few steps those are followed:

Step1 Bifurcating the Original Form into Chunks
Breaking up the original sentence into simpler chunks that can be smoothly translated.

Step2 Finding all Possible Translations for Each Chunk
This is done by finding all the distinct ways humans have translated those chunks/words not in the simple translation dictionary but in the training data which consists of actual translated words/phases of people in real-world scenarios.


The frequency of each translation occurring in the training data can be given a score and used at appropriate places.

Step3 Combining all Possible Sentences & Finding Most Likely One
Here all possible combinations are made to generate feasible outcomes of a sentence. ‘N’ number of counts is generated which is scanned thoroughly and only one that sounds ‘more human’ is deduced as the final output.

The sentences that are not similar to the data set are given a low probability score, however, the one which matches something in the training set, gets a high score and is finalized as the output.

This statistical method, nevertheless, requires human interference to modify/tweak this multi-step model. And this led brainiacs to develop much better-translating machines using two incredible ideas:

– Recurrent Neural Networks
– Encoding

These are some deep concepts with different contexts that many real-life implementations can also be used in the translation.

Programming, coding, designing seem to be very easy but these smart machines need to first understand how people learn languages and what they are. There are many different forms of communication where a lot of it comes down to learning it and practicing it.

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