Natural language generation (NLG), a wave of multi-billion dollar language startups is taking over the world. It’s revolutionizing how humans interact with machines and create content.

What exactly is NLG? What makes NLG different from other AI language technologies? Continue reading to learn everything you need about this revolutionary technology.

What is Natural Language Generation?

Natural language generation (NLG), is the process of turning data into natural language with artificial intelligence.

NLG software uses artificial intelligence models powered with machine learning and deep learning to convert numbers into natural language text, or speech, that humans can understand.   

Natural language generation is used by chatbots, voice assistants, and AI bloggers, to name just a few. NLG systems can convert numbers into narratives using pre-set templates. They can determine which words will be needed next, based on what you are typing in an email. The most advanced systems can create entire summaries, articles, or responses.

What is the difference between NLG and Natural Language Processing?

NLG refers to the process of using AI to translate data into text or speech. NLG is achieved through natural language processing (NLP).

Natural language processing refers to the accurate translation of what you say into machine-readable information so that NLG can generate a response.

The machine must “understand” the conversation or prompt to create a response. NLP can read (or hear), while NLG can write (or speak span.

What is the difference between NLG and Natural Language Understanding?

NLP is a system that converts what you say into data. NLG systems use that data to create language. What if the machine’s answer is not clear? Natural language understanding (NLU), is the solution.

Natural language understanding (AI) uses computational models to understand the meaning of human language. It uses NLP data to analyze the meaning of your words, and the relationships between concepts.

NLG creates a language that sounds like a human. NLU ensures that language that sounds human means something. If the NLU does its work, you will get a reply from a chatbot/voice assistant that makes perfect sense.

Natural Language Generation

NLG technology is used in a variety of commercial applications. You almost certainly use NLG technology every day, regardless of whether you are aware.

These are just a few examples of advanced NLG applications:

• Chatbots that answer your questions about websites.

• Voice assistants that respond to commands like Siri or Alexa

• Machine Translation Tools that translate one language to another.

• Conversational Ai Assistants who use advanced NLG or NLU to have two-way conversations

• Analytics You can use NLG to present insights from your data using an easy-to-understand language.

• AI bloggers can use language modeling to create content. It can automatically write any length of text, from a single sentence to an entire article.

• Sentiment analysis platforms employ NLU to determine which language resonates most with customers and then use NLG to create messages that they are likely to respond to.

• AI-powered transcription uses speech recognition to understand audio and NLG to convert it into text.

• Narrative generation tools use structured information (often in the format of a spreadsheet), to generate a text narrative.

These are only a few examples of how NLG can be used in business and everyday life. Let’s take a closer look at the specific companies that have developed NLG for these uses.

Natural Language Generation Tools

NLG tools can be used to use AI and machine learning to create and speak in commercial applications.

Marketing AI Institute tracks thousands of AI vendors. We have a great sense of the top tools that do NLG. These are just a few to look at:

Arria

Arria uses NLG for data extraction and summation to create readable narratives.

Automated Insights

Automated Insights makes use of NLG to create earnings reports at scale and sports articles (box scores/results, etc.). ), and data-driven stories.

Clickvoyant

Clickvoyant uses NLG to significantly reduce the time required to extract insights from analytic data and create presentations based on those insights.

Drift

Drift employs conversational NLG to eliminate friction in the buying process via chat, email, and automated products.

Exceed.ai

Exceed.ai uses AI for every sale lead in your pipeline. It uses human-like two-way chats and email to communicate with each one.

HyperWrite

Hyperwrite is an NLG tool that automatically creates sentences and paragraphs using prompts from a human.

MarketMuse

MarketMuse, an AI-driven assistant that helps you create content strategies and build content strategies, is available. It uses NLG to generate summary briefs that will help you write posts with maximum impact.

Narrative Science

Narrative Science uses NLG for structured data (spreadsheets) and turns them into human-sounding stories.

Pencil

The pencil uses NLG to generate more effective Facebook ads.

Persado

Persado uses NLG for marketing copy and creativity in order to provide the best messaging possible to your prospects across all channels.

Phrasee

Phrasee uses NLG for automatic email subject lines that are better than human writers. This results in higher open rates.

Yseop

Yseop uses NLG for automatically generating narratives from data from financial and medical reports.

Now you know a few NLG vendors, it’s time to get started with the technology.

Get Started With Natural Language Generation

It takes planning and thought to get started with NLG marketing and business.

These are the first steps to help your company adopt NLGs.

1. Find out if there is a need for basic NLG.

First, look at the stories that you are already telling with numbers. You can think of “stories” as any story that makes sense of data. These could include summaries, factsheets, and reports that are external-facing, or internally-facing.

Are you able to produce these kinds of stories often? Do you have any stories that are consistent and repeatable (e.g. You’re telling the same story each week )?

These could be candidates for natural language generation.

PR 20/20, the marketing agency behind Marketing Artificial Intelligence Institute has used NLG to reduce the analysis and production times of Google Analytics reports by up to 80%. Although this is not an advanced NLG use case that leverages GPT-3, it is still a valuable one.

This just shows that low-hanging fruits can create more value for your company and teach you the basics of natural language generation.

2. Take a look at the structure of your data.

Natural language generation requires structured data even when there is a use case.

Are your data organized in logical rows and columns? Our current NLG solution requires that you upload your CSV file. This is why data must be consistent and clean to make the most of this technology.

You may also need to spend time cleaning up your data before you upload it to a system using natural language generation.

3. Be realistic about your ROI.

NLG solutions, even the simplest, can take a lot of time to set up. A solution and any related NLG services will also require you to pay. It is important to look realistically at the technology and what it can do for your business, as well as how you can scale it.

First, analyze how long reports, articles, or narratives take. Then, see how much NLG could potentially cut down on that time.

Last, make sure to save time for all NLG-related staff. Your organization may be able to save an hour per week by reducing the time of each employee.

GPT-3 & the Future of Natural Language Generation

Recent advances in NLG are still very impressive. The field is rapidly moving at warp speed.

Research on NLG uses was published as far back as 1986. Ten years later, researchers from the University of Aberdeen published information about the use of technology to plan sentences and text. Even in 2006, experts still debated the obstacles to NLG adoption.

However, 2019 proved to be a great year for NLG. OpenAI, an AI research company that is not for profit, revealed that they had created an AI model that can produce coherent paragraphs of text on a large scale. GPT-2 was the name of the model and it was able to learn how to write well from analyzing 8 million pages.

GPT-2 was the inspiration for GPT-3. This model, which was released one year later, uses 100X more data than its predecessor and is 10x more powerful. GPT-3 is one of the most widely used NLG text generation models. It is increasingly being used to generate text almost indistinguishable from human-written sentences or paragraphs.    This means that you may not realize you are talking to a machine until you start a conversation online.