How Chatbots Learn on Their Own
Most individuals and organisations looking for chatbots are very interested in how chatbots from companies like Convertobot are able to self-learn. Contrary to popular opinion, self-learning bots are not the only chatbots that exist.
To truly understand how chatbots learn on their own it is crucial to know how chatbots are built. We will do so by reviewing the two major approaches as it pertains to chatbot development.
Retrieval Based Chatbots
Retrieval chatbots are designed using directed flows and graphs. The bot is programmed with a set of predetermined responses and it is trained to rate them on a spectrum.
The responses can either be input manually or be retrieved from an existing database hence the chatbots name. Retrieval based chatbot can also be designed to include third party systems.
Retrieval chatbots are the most ubiquitous chatbots on the internet. They are mostly utilised for customer support, generating leads and for feedback as they are goal-oriented bots.
You can decide the chatbot’s conversational tone and design the customer experience while maintaining the brand’s reputation.
Generative models are the other approach to building chatbots. They are usually based on neural networks.
Generative chatbots are not programmed with predetermined responses. Responses are instead generated from previous conversations with the user.
The generative approach to building chatbots is, therefore, very good for developing conversational chatbots. They will always have a response ready even when the user only wants to engage in banter.
Without many conversations from which to learn, chatbots developed using the generative model may at times have arbitrary responses that may even be senseless. Grammatical and syntax errors may also persist with these chatbots.
The main disadvantage of developing chatbots using the generative approach is that they require humungous amounts of conversational data to train them. Many chatbot customers do not have that amount of data which poses a significant challenge.
Generative Plus Retrieval Chatbots
You may find chatbots developed with a combination of the above approaches. Such a chatbot can have the best of both approaches.
It can be excellent in conversation while having the ability to providers users with a wide range of information. The approach you use will depend on what you want the bot to accomplish.
You can build a self-learning chatbot using both approaches stated above.
Since generative chatbots require massive amounts of data to train them, most developers have chosen to let the users themselves train the bot by engaging it in conversation. The downside is that training chatbots in this way can have negative consequences such as hate speech being programmed into a bot.
Training retrieval chatbots is quite different and comprises of several categories of self-learning. One aspect involves training the bot to recognise new intent. The bot will learn to recognise new intent from user conversations and add it to its knowledge base.
Training a retrieval chatbot to recognise new variations of queries is another aspect of designing a self-learning bot. Understanding context is another aspect that chatbots can learn through user conversations.
Completely self-learning chatbots are not yet a reality. Bot trainers are the best way to train chatbots to learn as of now. However, chatbot developers are getting better at training and maintaining their bots.