What Is Natural Language Interaction?

Natural Language Interaction (NLI) is the convergence of a diverse set of natural language principles that enables people to interact with any connected device or service in a humanlike way.

Increasingly known as conversational AI, NLI allows technology to understand complex sentences containing multiple pieces of information and more than one request. It can then react accordingly, creating value and enhancing the user experience.

Natural Language Processing

Natural Language Processing is used by NLI to split the input text into sentences and words and to normalize and pre-process it. For example, NLP might convert all the words to lowercase or correct spelling errors before determining if the word is an adjective or verb etc. and tagging this for future reference.

Natural Language Understanding

Natural Language Understanding (NLU) encompasses the building blocks to interpret human language. They are the base upon which both general and domain/client/project-specific Language Objects such as lexicon, synonyms and themes, NLU rules and dialogue flows can be constructed in the context of each NLI solution.

Natural Language Generation

Responding to a query using anything more that pre-scripted responses requires at a minimum, natural-language generation (NLG). This enables NLI to interrogate data, including integrated back-end systems and third-party databases, and to use that information in creating a response, combined with incorporating additional parameters which may be known, for instance user name, gender, location, time of day, appropriate tense, etc.

Why Syntax, Spelling, and Semantics Matter

Natural Language Interaction technology takes natural language processing (NLP) and natural language understanding (NLU) to the next level. It allows enterprises to create advanced dialogue systems that utilize memory, personal preferences, and contextual understanding to deliver a proactive natural language interface.

These advanced natural language processing applications remove the need for the customer to constantly repeat information during a conversation. The NLI interface can ask clarifying questions if there is any ambiguity. It also enables for the conversation to be interrupted by the user. A common situation when talking to another human, but one even Intelligent Virtual Assistants can rarely handle. Not only that, but natural language interaction can also bring the user back on track to the point of the original conversation.

In order to do this, NLI must understand exactly what the user means. This is a complex task. The way we talk in everyday conversation is full of subtle nuances. “I had a nice vacation in nice,” for example, shows how a machine must understand grammar, syntax and spelling mistakes.

How NLI Reveals the Hidden Value in Unstructured Data

But NLI isn’t just great for creating humanlike conversations, it’s also great for analyzing those conversations too.

When people communicate in a natural, conversational way, they reveal more than just the words they’re saying. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. This information is one of the reasons that makes the data collated during human-machine conversations so valuable.

However, enterprises frequently rely on their own, often prejudiced, interpretation of data, simply because they don’t have the necessary resources, or retrieving the relevant information takes too long.

Most conversational analytic tools interpret the data simply as words; without context, without meaning, and without frequencies. This results in information overload. It’s a flat view of the data where nothing stands out as more important.

An analysis of "Carry Me" Airlines conversational data, a fictitious name for an airline, but based on real data, showed that questions about baggage are one of the more frequent topics, however, when we drill down, it’s possible to see that customers use “baggage” and “luggage” differently. Luggage is much more likely to refer to carry-on bags. This type of information is tremendously useful when building an NLI app that is sensitive to the expectations of customers.

This is where analysis on unstructured data using NLI comes into its own because human intuitions about conversational data are often wrong. Businesses need the facts that NLI provides to guide them, otherwise enterprises risk misunderstanding the voice of the customer.

Analysing the conversational data also allows businesses to look for what customers don’t like about a service or product by bringing together concepts such as can’t, don’t, not etc. In the case of "Carry Me," one of the main issues was around printing boarding passes. A simple revision to the website solved this issue.

Natural Language Interaction Enables Faster Creation of Conversational AI Applications

From simple beginnings, natural language applications such as digital employees have grown to become the gateway to customer contact centers, the number one sales employee, the executive PA that updates corporate systems and the business analyst insights on markets and trends in real-time.

Natural language interaction removes the need for your customers to know and understand your terminology. It’s clever enough to figure out in over 35 languages what someone means when they use their everyday words and phrases, not yours.

While speech is a distinctive feature, it is how well conversational AI applications understand the complex sentences people use such as “Schedule a meeting with George for Thursday at 11, we’re meeting downtown” and more importantly, how accurately it responds that’s key to their effectiveness.

The deep understanding that Natural language interaction delivers gives enterprises the information they need to deliver a superior customer experience and have a positive impact on their bottom line. The fact that it automates the code that simulates the way a human thinks and makes creating a conversational AI application faster and easier than ever before is just an added bonus.