What Is a Generative Chatbot? What You Need to Know

Last Updated on October 12, 2020 by Sean B

Chatbots are a fascinating extension of AI technology and machine learning algorithms. But what is a generative chatbot? Like the name implies, it is a chatbot that generates answers. In other words, it doesn’t rely on a set list of questions and answers.

The best chatbots out there are frequently used in large firms and corporations as Virtual Agents for a variety of industries and uses including marketing, sales, financial services, healthcare services, education, entertainment, and customer service. But there are also chatbots called Virtual Assistants that we use every day at home. These include Siri from Apple, Amazon Alexa, Google Assistant, and Microsoft’s Cortana.

Chatbots have broken boundaries in terms of what we could achieve through AI technology; chatbots improve communication quality with customers in innovative ways and enhance the customer experience.

In today’s article, we shall be talking in detail about the chatbots and everything there is to know about them. Just read on!

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So What is a Generative Chatbot?

Well, a generative chatbot is a very efficient and smart bot as far as it’s learning mechanism is concerned. They can learn everything from scratch like an infant by using a process called Deep Learning. With these machine learning algorithms, a chatbot can learn through the data it encounters as well as the conversations it has with people and even other chatbots.

One thing to note is that there are two types of chatbots, one is the closed domain chatbot, meaning you are limited to a predefined (or closed) set of questions, and the other is an open domain, or a Generative chatbot.

The important distinction between the two is that the closed domain chatbot can only respond to a list of predefined questions with programmed answers, while the generative chatbot can generate responses on its own by making use of the previous data the chatbot stored as well as the previous interactions hence the name generative chatbot. If they don’t know something, they will usually state that and ask for more information. This is how they learn.

The chatbots are created so they can create a consciousness of their own using Deep Learning processes; it can be taught how to talk to people through text. A Deep Learning chatbot can also learn from movie dialogues, play scripts, essays, and literature.

However, the best way for a generative chatbot to learn is human interactions; the more an AI chatbot converses with a human, the more it can learn about the world, various topics, and the art of having conversations.

We talked a little about Deep Learning already, now it’s time to take a deep look at deep learning.

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What is Deep Learning?

The short answer is that Deep Learning is a subset of machine learning algorithms inspired by the human brain’s intricate functions. It attempts to create similar structures called artificial neural networks.

Deep Learning technology today is used in a wide number of services. This advanced technology has transformed our basic understanding about what AI can be and what it can do, and it continues to advance at a rapid rate.

Along with artificial neural networks, there are deep belief networks, recurrent neural networks, and convolutional neural networks that have been applied in a variety of fields such as machine vision, computer vision, speech recognition, natural language processing, and audio recognition.

What’s truly flabbergasting and wonder-inducing is that many times these Deep Learning bots have proved to exhibit feats comparable to and sometimes to surpass the human experts. Most specialists associated with Deep Learning will tell you there is a huge difference between biological neural systems and those in the AI, but the capabilities of Deep Learning are bringing the two closer every day.

One major difference between the two is that AI programs’ neural processes have a static network, while the biological neural system is dynamic and changing.

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What is the Best Way to Make a Generative Chatbot?

When it comes to building a generative chatbot that can ensure an interactive conversation between humans and a machine, there are plenty of Deep Learning processes you can choose from; however, we’ll only be discussing he most popular options that are available today. Now let’s get into the meat and potatoes of building a Generative Chatbot.

1. To Build a Generative Chatbot Accumulate and Prepare Data

The very first step in building your generative chatbot is to first accumulate preexisting data on your hand. You need to have tons of data available in your storage from human interactions or other alternative ways, such as play scripts or movie dialogues.

However, the most optimal method will be to collect tons of data from human interactions. Ensure that the data you have accumulated is unique, I.e., do not collect data of the same type of conversations rather than collect data from different types of conversations with different tones.

Pay attention to the details when collecting the data and make sure that the data you have collected would be the most optimal for your bot to learn from.

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2. Compile and Reshape the Data

This is conditional, and not everyone might need to do this one. But if your data is not varied or segregated, then you might need to compile it before you consider using it for your generative chatbot.

You need to add labels to the set of data you have received from the interactions, make pairs, and label who the speaker is and who is responding to the speaker in the conversations. This will help the bot to come up with a response.

Take care to follow some restrictions on the amount of data you have, such as you need to ensure that the conversation is only between two people, and the limit does not exceed. Separate the messages by at least five minutes of the gap between them.

3. Pre-Process the Bot

Pre-processing your chatbot means generating reliable outputs! You need to add some semantic recognition in your bot so that it can work efficiently, I.e., adding grammar into your bot’s consciousness so it can avoid the spelling errors and understand and interpret the human errors it encounters throughout its conversation.

Add all the necessary tools into the bot’s neural network so it can read the messages more efficiently. The common steps associated with pre-processing are tokenizing, stemming, and lemmatizing.
You can use online resources for all these steps as well as use online tools to accomplish the tasks discussed above

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4. Put Information on Word Vectors

To build a generative chatbot words vectors are critical. Word vectors are necessary to employ when the bot encounters words that frequently appear on social media yet are not a part of the official language databases. Such as LOL, WTH, etc.

You can either use pre-trained vectors to accomplish the task, or you can create your word vectors. The second method is more time-consuming yet as ah higher pay-off as that way, your bot will be able to handle the conversations more efficiently.

There is a Word2Vec model for this task. It can generate word vectors by recognizing the context in which the words are used. Thus by learning the use of words in sentences, the bot generates its vectors.

To train the Word2Vec model, you can use python script too.

5. Creating a Bot Model

The next step in the creation of generative chatbot is to start developing your bot model. Programmers developed a Seq2Seq model to accomplish the task. You can also use TensorFlow, which is an open-source programming library and is available for free to everyone.

The next step is to find an authentic python code and closely follow it. Follow the guidelines from the authentic source carefully and put the functions in your Seq2Seq model.

6. Testing and Checking

Practice makes perfect in almost everything we do, and it’s the same for your generative chatbot. After you have developed the seq2seq model, you need to perform tests on it, to check whether it produces the desired results. Track your process, i.e., check how the model behaves and check for additional errors in the bot’s behavior.

Check what tokens the bot returns and check the input the bot sends back at different points in the training process. Train the bot again and again, so it can eventually send back complex strings.

Initially, the data it will return will consist of simple strings, but the bot will generate more complex sentences as the process takes its flow and the chatbot learns.

7. Adding the Model to Applications

After you have tested your seq2seq model, the next step is to integrate it with an application. When finding the right application to integrate your model with, you need to pick a platform with enough traffic so it can interact with people with learning from them.

One of the quickest ways to achieve this goal is by choosing Facebook or messenger for the task. It also offers a simple method of adding a chatbot and is an optimal platform for your chatbot to learn human interactions from and grow.

Besides setting up important fields such as index.js files and other files necessary for your bot, you need to set up a server. There are many server applications available over the internet which offers their services.

Make sure to download all the dependencies as your tutorial from the authentic source guides you to.

You Are All Set!

So that was a brief, albeit slightly complicated overview of generative chatbots and their production process. So what is a generative chatbot? Well it’s a chatbot that you can ask any question you want, whether or not it can answer that question depends on a number of things. The technology behind the chatbot is the most important factor in determining how smart your generative chatbot will be, but the training you provide it is what gives it the education.

After developing a well-functioning generative chatbot, you can start deploying it to other platforms as well. There is always room for improvement; you can always keep an eye on the errors the bot produces, if any. And you can learn new skills to add to your chatbot, so it becomes more efficient at handling conversations.

We hope that today’s blog was informative; please feel free to share your remarks in the comments section below.

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