When you’re creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods.
With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team.
Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year. Many popular brands such as MasterCard have been quick to come up with their own chatbots too. Websites connecting chatbots with CMSs has also become a common practice, providing it with a rich source of relevant content.
But, before we get into how your brand can leverage such a chatbot, let’s look at what exactly a deep learning chatbot is.
A deep learning chatbot learns right from scratch through a process called “Deep Learning.” In this process, the chatbot is created using machine learning algorithms. A deep learning chatbot learns everything from its data and human-to-human dialogue.
The chatbot is trained to develop its own consciousness on the text, and you can teach it how to converse with people. Alternatively, you can teach the chatbot through movie dialogue or play scripts. However, a human-to-human conversation is the preferred way to create the best possible deep learning chatbot. Remember, the more data you have, the better the effectiveness of machine learning will be.
Now that you know what a deep learning chatbot is, let’s try to understand how you can build one from scratch.
The first step of any machine learning related process is that of preparing data. You need to have thousands of existing interactions between customers and your support staff to train your chatbot.
These should be as detailed and varied as possible so that there are ample data points for your deep learning chatbot. This particular process is called the creation of an ontology. Your sole goal in this stage should be to collect as many interactions as possible.
Depending on your data source, you may or may not need this step. If your data isn’t segregated well, you will need to reshape your data into single rows of observations.
These observations can be called message-response pairs that will be added to the classifier.
The goal of this step is to put one speaker as the response in a conversation. All of the incoming dialogue will then be used as textual indicators that can help predict the response.
You may need to set some restrictions while creating the message-response pairs, such as:
The conversation should only be between two people. This makes it clear who the message is directed towards.Separate messages that are sent within a minute can be combined into one message.To pair a message with a response, the response to the message must come within 5 minutes.
After the reshaping, your message-response pairs may look like this:
Hey, what’s up?Nothing much, enjoying the rain.Today’s been a tiring day.Same here. It’s been really hectic.
Once you’ve accumulated this data, you need to clean the data. You need to remove URLs, image references, stop words, etc.
The next step in building a deep learning chatbot is that of pre-processing. In this step, you need to add grammar into the machine learning so that your chatbot can understand spelling errors correctly.
The processes involved in this step are tokenizing, stemming, and lemmatizing of the chats. This makes the chats readable for the deep learning chatbot. You can use the NTLK tool for this, which is available for free.
In the final step of pre-processing, you create parse trees of the chats as a reference for your deep learning chatbot.
Once you’re done with the ontology and pre-processing, you need to select the type of chatbot that you’re going to create.