How NLP-based Cognitive Capabilities of AI Chatbots Increase Business Interactivity

As conversational AI technologies gain traction, they substitute hard processes, becoming valuable assets to businesses that are seeking to gain a competitive advantage among peers. Automated processes reduce the burden on employees in every office space by taking away the most time-consuming and mundane activities that plug creativity.  

A recent report from Gartner forecasts that more than 50% of enterprises will spend more per annum on bots and chatbot creation than traditional mobile app development in this year. 

Yet, even though the year-by-year adoption of conversational AI all over the world has increased, we still find that the general implementation is lackluster. Most implementations result in employees cleaning up after a chatbot interaction, and customers are dissatisfied with the responses they receive from the chatbots. The rule-based responses lack empathy and they can hardly make sense of real-life scenarios. 

One size does not fit all when it comes to chatbots. What differentiates mediocre chatbot performances with AI-enabled solutions is how well and if they implement NLP techniques. The concept of NLP in machine learning is employed to help machines make better sense of data in the way that humans did. The developments in the field of NLP are not recent, however, and can be traced back to the early 1950s.

A short history of NLP development and its impact in the present world

Even though computers were adept in reading text in the digital form, they were stumped by written text, natural speech and contextual information form external sources. They failed to process language in the ways intended and were not trained to interpret written text such as recognizing scribbles on paper. Moreover, converting speech to text and text to speech in a natural sounding manner was nearly impossible. Eventually, the funding for research in the field stopped, putting an end to all development.  

It wasn’t until the early 1990s when new machine learning (ML) capabilities such as rule-based parsing, morphology, and semantics became pivotal in the biggest discoveries that brought significant change in developing NLP. Deep neural networks and representation learning is driving the present-day NLP developments.  

Today, conversational AI chatbots can handle sales processes, software development, hiring, customer service, deliveries etc. According to Gartner’s Marketing Guide for Talent Acquisition, by 2022, 35% of organizations will turn the job application process into a simple conversation by utilizing conversational user experience and natural language processing in their recruiting process.  

Chatbots are now stepping in as best alternative to spending on additional resource investment for processes that are routine. Companies are finding other ways to innovate and recover the time that would be otherwise be lost performing mundane tasks.  

In order to find success in adopting chatbots into your business, you must choose carefully from among the multitudinous choices of solutions around you.

Common NLP techniques that power today’s AI chatbot technology

As companies are shifting their digital perspectives and see how they can benefit from modern technology, they learn to adopt superior technology even in the area of chatbots.  

That being said, the necessary components that make up better chatbots that are AI- are common NLP capabilities such as those listed below.   

Dynamic Text to Speech – While reading or giving voice outputs to texts, machines would usually be unable to produce natural-sounding speech. The speech could not emulate the effect of natural reading.  

With text-to-speech training with an ML model, machines or bots could learn to produce natural-sounding speech in various languages and accents. In addition to that, the chatbots are taught to give the necessary speech inflections, emphasis, and tone to their voice reading. 

Named Entity Recognition (NER) – NER is a basic technique that is used for information extraction on entities from the text.They are then categorized and sub-categorized into person, organization, brands, colors, geographies, etc. 

It’s a form of downstream processing that helps bots to interact in a better manner with humans, based on the information it gathers. This information could be derived using dictionary extraction, complex pattern recognition, regex extraction, and statistical methods from the structured text. These are used as tools for chatbots to perform with accuracy and recall.

Optical Character Recognition (OCR) – The OCR method is used to extract information from digital and non-digital copies of data. Sometimes, unintelligible writings can only be interpreted through logic. One example, funnily enough, would be doctors’ prescriptions that needed a trained professional to interpret.

By copying information from such complex sources into the computer systems, data can be safely stored and could be re-discovered at any time. This reduced the efforts of the professionals to digitize every input. Such data could also be manipulated to form further interpretations and insights.  

Contextual Extraction – Unlike humans, a bot or machine cannot understand common sensical information if they are not categorized under the right columns and rows in a database. However, with contextual training, machines could automatically understand structured information from unstructured sources.   

Otherwise classified as dirty data, machines are now linguistically trained to follow contextual information in conversations, memorize previous information, interpret and reply to natural human speech. This means that the machine won’t respond with an apology as often as it did with previous chat experiments when they don’t understand a question. They can now understand and also index information to detect duplicates as they occur.

Sentiment Analysis – Sentiment analysis is an interesting method which gained much attention in the recent past, in which conversational bots were exposed to human like sentiments. They are trained to recognize and show emotions much like human beings. The most famous example here would be the Sophie Bot.   

To make the line of differentiation between a bot and humans thinner, bots had to display normal human expressions in language that would induce warmth into conversations. This also involved forming opinions, reacting to situations in engaging manners and identifying moods during interactions. This would be a huge leap in making interactive bot displays and chats more customer-friendly.  

Integrating AI chatbots with your business

Conversations with chatbots need to be more like in-person interactions to raise customer satisfaction and business response. Replacing rule-based chats that achieve nothing with intelligent Virtual Agents (VAs) will propel your business into new and unforeseen heights.   

Innominds has partnered with a leading conversational AI provider, Haptik, which is part of the $65 billion internet conglomerate, Jio Platforms Ltd, to help businesses scale up conversational AI adoption to enhance customer experiences and build the foundation for a future of contactless systems and seamless self-service transactions and processes. 

Our conversational AI digital assistants come with 600+ pre-built intents for banking and insurance sector, enabling many industry-specific conversations. This not only reduces the workload up to 70% on the repetitive queries but also improves the user engagement by 500%.  

Innominds practice engineers are fully trained to assist businesses with Haptik’s intelligent virtual assistant implementation as well as to provide seamless integration within the existing ecosystem.