Clearly, chatbots are here to stay. Not all are made equal, however – the choice of technology is what sets great chatbots apart from the rest.
Intent detection – what is it?
In simple terms, intent detection is the process of algorithmically identifying user intent from a given statement. That’s a lot of words to describe a rather simple process, so let’s take a look at an example.
Let’s say the user says a phrase like "I’d like to book a ticket for Paris on the 27th of April."
How intent detection works – step by step
Of course, while the concept may be simple, its implementation has proven challenging. AI researchers have been building chatbots for well over sixty years. Yet truly responsive chatbots are a relatively new achievement.
The way intent detection works is complicated and often daunting to newcomers. We’re not going to lie – there’s a lot you need to wrap your head around.
Here’s how a rudimentary chatbot detects user intent:
- Take user statement
- Scan the statement to search for keywords: for every word in the statement, look for an association in the chatbot’s database
- If there’s a conflict – for instance, two different intents are detected – make a decision on which one to present to the user, whether randomly or by other means
- Send an appropriate response
The challenges of intent detection
One of the biggest challenges in building successful intent detection is, of course, natural language processing. That in itself is a vast field requiring expertise and cooperation between computer science and linguistics. For that reason, we at SentiOne collaborate heavily with linguistics departments at leading European universities.
Industry examples of intent detection
You don’t have to look far to find examples of good intent detection – Apple’s Siri and Amazon’s Alexa are both examples of very strong intent detection. These are chatbots designed to take care of a wide variety of tasks – looking up information, playing media, controlling smart home devices, and more. They need to understand user intent across a variety of contexts, accents, and (most importantly) functionalities.
The business value of good intent detection
Good intent detection results in good chatbots. By employing state-of-the-art intent detection, your chatbots can handle even complex or ambiguous tasks – allowing you to extend their functionality endlessly.
A closer look: the transformer-capsule model
So how does SentiOne do intent detection? We asked our AI researchers to explain:
Our AI team has developed a model with intent accuracy recognition at 0.9889 – exceeding previously existing solutions, which was shown on the well-known ATIS dataset and featured as number 1 on Papers with Code. The solution is based on a Transformer-type neural network, used in the BERT model as well, that has recently triumphed in the field of machine learning and natural language understanding. We have developed this approach in order to obtain better results by combining it with so-called capsule networks, previously used in the field of image recognition. This type of neural network performs really well in modelling relationships between concepts and allows for better understanding of natural language
Conclusion
In conclusion, intent detection is the key to building successful chatbots. By employing state-of-the-art intent detection, your chatbots can handle even complex or ambiguous tasks – allowing you to extend their functionality endlessly. Chatbots like that reduce the workload on your customer service department, since agents aren’t bogged down by repetitive tasks every single day.
FAQs
- What is intent detection in chatbots?
- Intent detection is the process of algorithmically identifying user intent from a given statement.
- How does intent detection work?
- Intent detection works by scanning the statement to search for keywords and making a decision on which intent to present to the user.
- What are the challenges of intent detection?
- The biggest challenge is natural language processing, which requires expertise and cooperation between computer science and linguistics.
- What are some examples of good intent detection?
- Apple’s Siri and Amazon’s Alexa are examples of very strong intent detection.
