How do chatbots work and what is the technology behind them?

What are the types of chatbots?

First, let’s take a look at the main types of chatbots and explore the different ways they process human languages and human interactions.

Rule-based chatbots are simpler forms of chatbots with pre-defined rules. They’re mostly used in instant messaging apps to perform automated customer support. These chatbots can detect common phrases for answering simple questions, such as booking a restaurant, buying movie tickets, or purchasing online services.

Guided by a decision tree, the virtual assistant gives customers a set of pre-defined options that lead to the desired answer. If a query falls outside the pre-defined rules, the chatbot won’t be able to assist further and will have to end the conversation.

The other type is AI chatbots, which use natural language processing (NLP) technologies to understand the intent or context behind the question and solve the customer’s problem entirely without any human assistance.

The biggest difference with the rule-based chatbot is the usage of the machine learning models that significantly increase intent recognition and guide the chatbot for future interactions through text classification or training data. Through API integration with the back-end systems, chatbots can even automatically perform the tasks for the customers when asked.

Rule-based versions are not necessarily inferior to AI ones. It all depends on the business objectives.

If the service is pretty straightforward, rule-based ones are possibly all that is needed. However, as companies start to offer more complex and interconnected products and services, the need to have a chatbot that can handle multiple queries and recommend relevant products becomes necessary.

How do chatbots learn?

Simply put, chatbots learn through processing data (questions and responses) or a knowledge database (stored information). By feeding computers large amounts of data, AI can anticipate how humans think and react, and mimic human intelligence. Chatbots learn through exposure to syntax, word usage, how satisfied the customers were by previous answers, and even sentiment analysis, where chatbots interpret emotions based on the terms a user sends.

Aside from sentiment analysis, algorithms can train chatbots to detect anomalies or fraud, recognize speech and voice patterns (conversational IVR), and predict additional queries, which can be helpful in FAQs. Pattern matching, or learning from past conversations, allows chatbots to look for a related pattern that matches a customer’s behavior. Compare this with human agents who can remember only a limited number of conversations. By analyzing past and real-time data, chatbots and other AI systems learn through experience rather than active coding/programming.

Although chatbots can learn without human intervention, a real person is still initially needed to check the responses to ensure they’re still on the right track. This is called “human in the loop.”

However, as chatbots are exposed to relevant data and the different ways that users communicate, they would require less and less maintenance. As they start to build a neural network, they become more self-sufficient. Without humans in the loop, machines can learn even unnecessary things, that is why only this can secure high-quality results.

How do chatbots work?

We’ve established that chatbots learn through data exposure and experience. Let’s take a closer look at all the components that make this possible.

Chatbots use AI and machine learning to understand human words, or a human conversation, and use pre-build solution flows that help to give a solution. 

All about natural language processing (NLP)

Not all chatbots are made equal. Rule-based models, in particular, depend on a pre-defined answer for certain keywords. More sophisticated AI-enhanced chatbots use Natural Language Processing (or NLP) to read, interpret, understand, and respond to human speech.

NLP is a branch of AI and machine learning that explores the way machines analyze and interpret human language to provide context-based responses and improve their performance over time. It’s behind many of the NLP applications we use on a day-to-day basis, such as automatic translation, text-to-speech, autocorrect, predictive typing, and virtual assistants such as Siri and Alexa.

There are more than 7 billion people on the planet, and the millions of conversations they have per day generate huge volumes of text-heavy, unstructured but potentially valuable data. By bringing together computer science, linguistics, cognitive science, and data science, NLP builds deep neural networks that facilitate human-to-machine communication by allowing machines to obtain and process that data.

How do chatbots work with regards to NLP? There are three main building blocks involved in building a chatbot, two of which are NLP subsets. Let’s take a closer look.

How do chatbots use natural language understanding (NLU)?

We humans can usually understand each other even when the sentences we write or pronounce aren’t grammatically perfect. Unfortunately, the same can’t usually be said for machines, which can be easily floored by misspellings, mispronunciations, or contractions, not to speak of colloquialisms. How do chatbots work when it comes to understanding typos, hurriedly-typed out requests and just generally finding out what it is a user means to say? 

This is where natural language understanding (NLU) comes in. NLU is a deep and complex subject that has been fascinating researchers for several decades – the first known attempt at an NLU program dates back to 1965. Specific examples of NLU include machine translation, automatic routing of customer service tickets, and automated reasoning, i.e the ability to make logical inferences based on previous examples. Basically, NLU is what enables us to be understood by machines, without having to type code into our smartphones (and if you’re wondering what the difference is between NLP and NLU, the answer is simple: NLU is a subset of NLP.

Most NLU systems are based on three main aspects:

1. Syntactic parsing, i.e syntactic analysis of a sentence in order to identify the function of each word and how they relate to each other.
2. Semantic parsing, in which the meaning of words are identified independently of their context.
3. Contextual interpretation, which removes any remaining ambiguities.

Various artificial intelligence classifiers and other techniques are used to achieve the above. In a nutshell, Natural Language Understanding is what enables chatbots to gauge intent and convert it into structured data that a machine can understand, thus going beyond simple keyword recognition.

Where natural language generation (NLG) comes in

So far, we’ve found out what enables chatbots to understand and interpret language, as well as how they process information and create conversation flows that feel natural and deliver relevant information. But how do chatbots work to produce text and speech that’s comprehensible to humans?

NLG or Natural Language Generation is another subset of NLP. Its purpose is to transform structured data into natural language, i.e text that us humans can easily read and understand. NLG can be understood as the opposite of Natural Language Understanding: while the latter makes human speech or writing understandable to machines, NLG converts data back into plain English (or any other language). When it comes to ai chatbots, both NLP subsets are necessary in order to produce a smooth and coherent conversation.