NLP Unplugged: How Conversational AI is Creating the Next Generation of Chatbots

June 2023

Chatbots

Fred Savage

Senior Market Data Analyst

As we discuss in our latest chatbots research, most chatbots are integrated with NLP technology, which allows the chatbot to determine the meaning from language. This is where natural language is broken down and converted into data elements; enabling the computer to decipher its meaning.
 
NLP is a subfield of linguistics and AI, and its aim is to supply machines with the ability to understand and respond to both tech or voice data. This NLP does not only allow the chatbot to understand this language, but it also allows for the chatbot to reply in the same medium, similarly to how humans converse. NLP combines both statistics with computational linguistics, machine learning and deep learning models. This allows for the chatbot machines to process human language in the form of both text and speech and be able to understand its full meaning.

Natural Language Understanding

NLU is a further subset to NLP, and its aim is to extend its machine linguistic capabilities.
 
NLU uses different algorithms to interpret the natural language, derive meaning, identify context, and draw insights from the text or speech data. It is also able to understand how the same words may have different contextual meanings and overcome language flaws, such as spelling mistakes, thus removing the ambiguity recognised by NLP.

Conversational AI Maturity

Conversational AI combines NLP and NLU with traditional conversing software such as chatbots, voice assistants and interactive voice recognition systems, and this is communicated through a spoken or written interface.
 
Juniper Research has identified three models for conversational AI:

Scripted Chatbots

Scripted chatbots are rule-based chatbots that use decision trees to utilise a set of manually pre-defined rules to respond to customer questions, however, these chatbots only have a limited number of responses. These chatbots are built to respond to specific types of questions such as a brand or enterprises’ FAQ (Frequently Asked Questions) section. 
 
In these scripted chatbots, each command is written independently and if the user’s query does not align with these pre-defined set of commands, or if the query does not have relevant keywords, then the chatbot will respond with an error message.
 
Although these chatbots are beneficial, as they are quick to develop and can answer a large proportion of simple customer queries, they are limited in their application. The fact that users have to enter specific keywords in order to get an answer from this chatbot can lead to user frustration, which in turn, can lead to the user no longer engaging with that brand or enterprise.

NLP Chatbots

NLP chatbots can respond to customer queries with a larger array of suggestions due to the inclusion of machine learning capabilities.

Using syntax analysis, NLP chatbots are able to deconstruct the user’s input and analyse its meaning to provide the most appropriate response or action.

Contextual Chatbots

These chatbots are the most advanced type of language processing. Contextual chatbots use AI and machine learning to remember past conversations and are capable of learning patterns of behaviour in order to adapt over time.
 
These contextual chatbots use NLU to understand language semantics to decipher sentiment and intent. Moreover, these chatbots are capable of contextualising within the conversation and are therefore able to circumnavigate mistakes in spelling and grammar in order to keep the conversation flowing.

Want more insights and statistics?

Download our latest chatbots whitepaper, which evaluates how open language technologies, such as ChatGPT, will disrupt the provision of chatbot services. You can also visit our infographics area, where you'll find an infographic containing our latest chatbots market statistics.