Natural Language Generation Myths the State of Natural Language Generation in Content Marketing: Everything You Need to Know

Natural Language Generation (NLG) is taking the world by storm as a wave of multi-billion dollar language startups revolutionize the way humans create content and interact with machines.

But is it NLG at all? And how does it differ from other AI language technologies?

At the Marketing AI Institute, we've spent years studying AI technologies and their impact on marketing, including NLG. We've compiled our experience into this post, which covers everything you need to know about this transformative AI technology.

 

natural language generation myths the state of natural language generation in content marketing

What is Natural Language Generation?

Natural language generation (NLG) is the process of converting data into natural language using artificial intelligence.

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

AI chatbots, voice assistants, and blog writers (to name a few) use natural language generation. NLG systems can convert numbers into narratives based on pre-set templates. They can predict what words to generate next (say, an email you're actively writing). Or, more sophisticated systems can generate full abstracts, essays, or responses.

 

What is the Distinction Between NLG and Tongue Process (NLP)?

NLG is the method of translating knowledge into text or speech victimization AI. Natural Language Processing (NLP) is what feeds the data to NLG.

Natural language processing is the process of accurately translating what you say into machine-readable data so that NLG can use that data to respond.

After all, the machine has to "understand" the conversation or prompt to come up with an answer. Put another way: NLP reads (or listens) while NLG writes (or talks).

 

What is the Distinction Between NLG and Linguistic Communication Understanding (NLU)?

NLP translates what you say into data. The NLG system uses this data to generate language. But what if the machine's answer doesn't make sense? This is wherever linguistic communication Understanding (NLU) comes into play.

Natural language understanding is an AI that uses computational models to interpret the meaning of human language. Analyze the data generated by NLP to understand the relationship between the meaning and concepts of your words.

NLG creates a language that sounds human. NLU ensures that human-sounding language actually means something. If the NLU does its job, you get a response from a chatbot or voice assistant that makes a lot of sense.

 

Applications of Natural Language Generation

NLG technology has countless business applications, and you almost certainly experience NLG on a daily basis, whether you realize it or not.

This area unit simply a couple of samples of advanced NLG applications:

• Chatbots that automatically answer questions on websites.

• Voice assistants like Alexa or Siri answer commands.

• AI conversational assistants that use advanced NLG and NLU to conduct two-way conversations.

• Analytics platforms can use NLG to express insights from their data in easy-to-understand language.

• AI for content creation Blog writers can use a language model to automatically write anything from a sentence to an entire article.

• Sentiment analysis platforms use NLU to understand what language resonates with users, then use NLG to create messages they are likely to respond to.

• AI-powered transcription tools use speech recognition to understand audio, then NLG to convert it to text.

• Narrative generation tools use structured data (often in the form of spreadsheets) to automatically generate textual narratives.

These are some common ways to use NLG in business and consumer life. Now let's take a look at some of the specific companies developing NLG for these use cases.

 

Natural Language Generation Tools

There are thousands of NLG tools that use artificial intelligence and machine learning to write and speak in business applications.

Getting started with NLG in business and selling needs some thought and design.

Here are some initial steps you'll go to accelerate NLG adoption in your business.

 

1. Determine If You have a Use Case for the Underlying NLG

First look at the stories you are already counting manually with the numbers. Think of "stories" as any narrative that makes sense of data. This may include external or internal reports, summaries, fact sheets, etc.

Do you produce these types of stories regularly? Are any of these statements consistent and repetitive (ie, are you reporting or telling the same kind of numbers every week or month)?

These may be candidates for natural language generation.

PR 20/20, the marketing agency behind the Marketing Artificial Intelligence Institute, has used NLG to reduce the time it takes to analyze and generate Google Analytics reports by up to 80%. It's not a sophisticated use of NLG to take advantage of something as robust as GPT-3, but it's valuable.

It just goes to show how low-hanging fruit can quickly create value for your organization while teaching you the basics of natural language generation.

 

2. See How Your Data is Structured

Even with a use case, natural language generation requires structured data to work.

Are your data sets organized into neat columns and rows? The current NLG solution we use requires a CSV upload, so the data needs to be clean and relatively consistent to get value from this technology.

Or, you may need to spend time cleaning your data before loading it into a system that uses natural language generation.

 

3. Be Realistic About Your ROI

NLG solutions, even basic solutions, typically require considerable time to configure. You must also pay for the solution and possibly related NLG services. You'll want to require a sensible look into the technology, what it will do for you, and the way abundant you'll scale victimization.

Start by looking at how much time reports, articles or narratives currently take, then see how much time NLG can save.

Finally, apply those time savings to all staff who will be affected by NLG. An hour saved per employee per week makes financial sense for your organization.

 

GPT-3 and also the Way Forward for Language Generation

The most impressive advances in NLG have occurred recently, and the field is moving at the speed of light.

As early as 1986, research was published on the potential use cases of NLG. Ten years later, researchers at the University of Aberdeen published the use of technology for text and sentence planning. As of late 2006, barriers to NLG adoption were still being defined and discussed among leaders in the field.

However, 2019 turned out to be a real banner year for NLG. At the same time, OpenAI, a non-profit AI research company, announced that they had created an AI model that basically writes coherent paragraphs of text to scale. The model was called GPT-2, and it learned to write by analyzing eight million web pages.

The GPT-2 spawned the GPT-3, a model released just over a year later that uses 100 times more data than its predecessor and is 10 times more powerful. GPT-3 is one of the most popular NLG text generation models in use today. It is used to rapidly create text that is almost indistinguishable from human-written sentences and paragraphs.

This means that pretty soon, the next time you have an online conversation, you won't even realize you're talking to a machine.

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