The biggest time saver in bot building since the introduction of AI
This week Chatlayer launched a major upgrade to its platform. The Chatlayer 3.0 release brings many improvements. We now have built-in voice bot functionality, versioning, synonym entities, a code editor, an iFrame module and advanced NLP analytics that tell you where and how you can improve your NLP even before you launch your bot. But 3.0 comes with yet another unique feature: language independent NLP. The biggest time saver in bot building since the introduction of AI.
What's in it for me?
Language independent NLP makes it a lot easier for you to build bots in more than one language. If you build a bot in for example English and you properly train it’s NLP in that language, it will already understand more than 100 other languages out of the box. Yes, you read that correctly. Our NLP is actually language independent. Here is an example for our Choo Choo bot. If you have ever followed our tutorial, you know this bot that lets you order train tickets. Since the tutorial is in English, you might think your bot only understands English. But that is no longer the case. Go into the NLP – Test tab and type the following sentence in English: “I want to book a train ticket”. As you can see in the screenshot above the model is 99% confident that you want to book a train ticket. Now repeat the same for all the other languages you might think of you can use Google Translate or even better DeepL to give you the correct translations for languages you don’t speak.
- French – “Je veux réserver un billet de train” – 99% confident
But how does this work?
If you have ever worked with Google Translate you know that the system is not perfect. And even the more powerful DeepL makes weird mistakes from time to time. So how is it possible that our NLP can understand so many languages? And that without specifying the language up front?
The difference with our NLP and a translator is that our engine doesn’t translate into another human language. Our engine translates whatever a user says to a machine language. And our NLP uses this machine language to determine their intent.
What is special about our NLP is that you don’t have to specify which language you are using. Our NLP maps it correctly automatically, whatever the language. So the era of one NLP model for every language is finally over!