NLG: Reduces Communication Gap between Humans and Machines

NLG accepts input in non-linguistic format and turns it into human understandable formats

Almost every industry today is looking to embrace familiar technologies like artificial intelligence, machine learning, big data, etc. Furthermore, they reap the advantages of its subsets with an intelligence-driven system that captures, processes and synthesizes data, resulting in automated data analysis as well as content management. Content automation is one of the major applications that businesses unravel to minimize their human labour spent on it. Rather than writing thousands of different descriptions for their catalogue, companies rely on Natural Generation Language (NLG) to convert structured data like product specs into a description that is easier for humans to consume.

Humans and machines have a similar level of functioning when it comes to learning a new language. If humans are endeavoured to learn a foreign language, the first progress they see in their skill is improvement in understanding it. However, they can't frame sentences and reply or write at the same proficiency. That is because the process of generating words and sentences is much more complex than that of understanding. When we compare human language understanding mechanism with that of machines, natural language generation is complicated than natural language processing (NLP). While NLP enables machines to understand what humans say or type, NLG generates outputs in form of text or speech.

What is Natural Language Generation (NLG)?

Natural Language Generation (NLG) is a subdivision of artificial intelligence (AI) that aims to reduce communicative gaps between machines and humans. NLG accepts input in non-linguistic format and turns it into human understandable formats like reports, documents, text message, etc. NLG algorithms are meant to automatically generate text from structured data that reads as though the generated text that was written by a human author. Structured data refers to product reports describing the features of a new product, survey results from online customer report, financial reports and personalized emails. By leveraging NLG, businesses can generate thousands of pages of data-driven narratives in minutes using the right data in the right format. A Gartner report published in 2019 predicted that by 2022, 25% of enterprises will use some form of natural language generation technology.

NLG research often focuses on building computer programs that provide data points with context. Advanced NLG software can even mine large quantities of numerical data, identify patterns and share that information in a way that is easy for human consumption. Particularly, the speed of NLG software is useful for news production and other time-sensitive stories on the internet. For example, Gmail uses LG to suggest appropriate sentences. Every time you type content and pick a suggestion, NLG learns your pattern of writing. Henceforth, when you write a mail, it predicts what you are trying to say next in real-time.

The Associated Press and other media outlets are using NLG robot journalism programs for many years to give datasets context. Using the technology, they create machine-written corporate earnings reports. Earlier, human reporters used to go through all the earnings report files when they are out. Today, NLG ingests the data, then produce a narrative in seconds, freeing up reporters to pursue higher intellectual works.

In 2019, OpenAI, a non-profit AI research company announced that they have come up with an AI model that essentially writes coherent paragraphs of text at scale. The model called GPT-2 learned to write by well analyzing eight million web pages. In 2020, the company updated its model and released GPT-3. These kinds of mechanism powered by NLG have serious implications for content marketers.

Applications of Natural Language Generation

Enabling analytics dashboard

One of the major aspects to look for when you are running a business is time. Business leaders value time more than anything else and they ask for to-the-point solutions. They require information in an easily comprehensible format so that they can make quick and effective decisions. NLG analytics dashboards can help them with tools that can be used to interpret the data generated from analytics in concise, yet, comprehensive reports.

Converting tabular insights into narratives

The financial market oscillates a lot and its complex terms are difficult for laymen to understand. However, an analyst could easily read out stock analysis report, equity reports, stress test report, etc. and come to a conclusion. Fortunately, NLG software is being used to make things simple and in a conveyable manner for normal public.

Enhancing the ability of chatbots

Chatbots largely depend on the conversational data inputs fed into the mechanism. The best a chatbot could do is to give humans the feeling that they are conversing with a fellow human being. Even though chatbots mainly require natural language processing capabilities, natural language generation adds more efficacies to it. The chatbots featured with NLG are context-sensitive and adept at personalizing user experiences, helping businesses to automate their customer service verticals.