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Chatbot Development: How To Approach A Self-learning Bot?

Chatbot Development
Chatbot Development

Can bots learn on their own? This is one of the most popular queries that must have crossed your mind while analyzing chatbot development. Experts believe that a bot is truly considered intelligent, only when it is able to inculcate knowledge on its own. Since the half-hearted approach is considered destructive, let us learn and examine the meaning of the above-mentioned question. But before that let us understand the basic concept of chatbots.

What is Chatbot?

It is basically an artificial intelligence software that can start a conversation with the user in a common language with the help of any messaging application, mobile apps, websites or even through the phone.

Why They Are So Important?

It is one of the most revolutionary, advanced and promising expressions of establishing communication between the machines and humans. But if we consider its definition from a technological aspect, a bot only represents a Question-Answering system that leverages on Natural Language Processing(NLP). Creating the most accurate responses to the questions is one of the most sporadic examples of NLP, that is applied in an end-user application.

Understanding The Concept Of Chatbot Development

1. Retrieval Based

So the bots based on this technology work on the principle of directed graphs or flows. It is trained to provide the best-suited response from a finite set of predefined responses. All the responses are either entered manually or based on the previously existing information.

They permit the bot developers and user interface to control the experience and then match it according to the expectations of the users. They are well known for their work in customer support, feedback and lead generation. Now developers can design the complete experience and also select the tone of the bot. But all this must be done while keeping the brand’s reputation in mind.

Even though retrieval-based chatbots are the most popular ones, they are definitely not the only kind.

2. Generative Model

This is another method of moving forward in the chatbot development and is based on the seq2seq neural network. Despite being released for machine translation, this network has worked effectively in developing generative chatbots. Unlike retrieval based bots, they are not based on predefined responses. But instead of this they are trained using a gargantuan number of conversation and based on that the responses are generated. This technology is best for developing conversational bots, with whom the user is basically looking forward to having some witty talks.

They are always ready with a response to any question. But if you are thinking that there are no shortcomings to this model, then you are sadly mistaken. In many cases, users might get a random response, or it might provide syntax or grammatical error.

One of the biggest requirements is the huge amount of conversation, in order to train. But most of the users or customers don’t have such a large amount of data.

3. Combining The Retrieval and Generative Model

Adopting a single technology is sort of an archaic concept. Because now the world appreciates the infusions of concepts or technologies. Therefore, companies need to shift their sporadic approach and are a model to relish the best of both worlds.

For improving the small talk capabilities, the generative model is used. Through such models, it is easy to customize the tone of the small conversation. However, the main focus of chatbots is to understand the goal of the customer and provide them with accurate information. And for that, retrieval based models are fully capable.

4. How To Build Self-learning Bot For Both The Systems?

Now, generating systems are used to simply provide the answer to a particular question, that might have to be missing from the initial data. In order to inculcate the feature of self-learning in the bots, developers have adopted an approach that lets users train them.

But sadly it has had multiple numbers of unexpected consequences when in one scenario users started to retrain the bot with the hate speeches. Hence, it is clear that self-learning generative models can be disastrous when allowed to be reprogrammed by the users.

Below mentioned are a few categories of retrieval based self-learning. Take a look…

1. New Milestones To Achieve

The bots might have been designed to accomplish a particular objective. For example, a bot can only book a pizza, but cannot cancel it. Therefore, developers need to integrate all the aspects.

2. Missing Data Or Variants

The system reflects the missing data by recommending new variations for a specific aim that is similar to the existing intents.

3. Contextual Word Representations

This is a continuous process to improve the embedding or in other words to enhance the vocabulary of the chatbots.

In a nutshell..

We cannot deny the fact that with this speed of development, we might have multiple self-learning chatbots like ‘Jarvis’, in the Avengers movie. But for now, the production of such intelligent chatbot development is not ready, as there is a lot that is required to build such mind-blowing creations.

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