A Quick Look At Natural Language Generation (NLG)

If you’ve used any of the Large Language Model-based AI writing tools, you must have asked yourself: Is this machine sentient? How can this program not only understand me but also respond accurately?

Well, Natural Language Generation (NLG) is the answer. It uses Artificial Intelligence features (duh!) that allow computer systems to produce written or spoken outputs. NLG is a human-to-machine and machine-to-human interaction – that is, it allows a human user and a machine to have a conversation in the same language. Aren’t you glad you don’t have to learn binary?

This technology combines Artificial Intelligence with Computational Linguistics to construct computer systems that can generate understandable texts in human languages. Naturally, this tech can be applied anywhere we rely on interaction with a computer.

So, let’s take a closer look at Natural Language Generation!
 

Alexa, Tell Me About Natural Language Generation!

That was our way of saying that even voice assistants such as Alexa and Siri use NLG to converse with their human users. NLG is driven by Artificial Intelligence to produce natural written or spoken language as an output. Yet, you must be asking – don’t computers exclusively understand binary language, that is 1s and 0s?

Well, yes – but NLG is a little like Pinocchio, the wooden puppet who came to life and could talk. Except there are no magical spells involved, it’s just good old technology. NLG models know the relationship between words in human languages, which they use to understand the user’s intent.

Then, AI transforms the required output from binary into comprehensible sentences which the user can understand. (Okay, maybe there is a bit of magic involved!)

NLG is hailed as the future of human-computer communication – before looking at this futuristic technology, let’s take a quick look at its history.
 

Evolution And Origins Of Natural Language Generation

While it’s challenging to point out the exact start of NLG, it has existed since the early 1960s. NLG was first used commercially in the late 1990s but only found mainstream adoption in the late 2010s. Here’s a quick look at the timeline:
 

• 2007

  Robbie Allen creates StatSheet, a sports website offering comprehensive statistics for college basketball. It automatically published real-time updates, game previews, recaps, injury updates, etc. using an automated publishing engine.

• 2010

  The first NLG-powered content was created by StatSheet and they also submitted and received approval for the first NLG patent. Other businesses were experimenting with NLG but adoption was slow.

• 2012

  Yahoo! Sports Fantasy Football introduced automated NLG coverage, which included personalized draught reports and game recaps.

• 2013

  Allstate's adoption of Automated Insights NLG technology marks NLG's foray into the business intelligence (BI) sector.

• 2014

  At Google for Entrepreneurs Demo Day, the American technology company Automated Insights announced the release of its NLG software, Wordsmith. According to reports, it helped Automated Insights produce 300 million pieces of content in 2013, more than the combined output of all major media companies.

• 2015

  Gartner categorized Natural Language Generation as a separate technological field.

• 2016

  Automated Insights launched Wordsmith as the world’s first self-service NLG platform.

• 2017

  When Amazon and Automated Insights hosted the Amazon Alexa Hackathon, NLG partnerships started emerging in the market. MicroStrategy and TIBCO collaborated on NLG projects during this time.

• 2018

  The availability of Automated Insights' Wordsmith Extensions for Tableau cemented the combination of NLG and BI tools. Additionally, Automated Insights received SOC 2 Compliance, which established a benchmark for NLG-powered solutions.

Since then, Natural Language Generation has seen high adoption and even higher investments. We mean, NLG tools are being used by more than 50% of Fortune 500 companies today!
So, how does it help them?
 

How Does Natural Language Generation Work?

 
Natural Language Generation is a multi-stage process, with each step further refining the data to produce an output with natural-sounding language. The six stages of NLG are as follows:
 

• Analysis Of Content

  The data is filtered to establish what should be included in the final product. This step entails figuring out the main themes and connections between the source document's topics.

• Understanding The Data

  Relationships between words are discovered after the data has been interpreted. At this point, machine learning is frequently used to contextualize the content.

• Structuring A Document Plan

  A document plan is created to finalize a structure for the output, based on the type of data that is interpreted yet.

• Summarization

  The topic or theme is succinctly summarized using pertinent sentences, phrases or fragments of sentences.

• Grammatical Structuring

  Grammar rules are used to create text that sounds natural to the user. The program interprets the syntactical structure and rewrites the output in a grammatically sound way.

• Language Presentation/ Output

  The final output is then generated, based on the format the user has selected; that is either written or audio output.

For example, in the healthcare sector, NLG solutions allow medical professionals to generate summarized patient reports based on detailed information about their medical history. Or, in the EdTech sector to create personalized learning plans and curricula based on the student’s profile.

However, this is just the tip of the iceberg – here are some other applications of NLG!
 

Applications Of Natural Language Generation

We bet no matter what your profession is, NLG can play a vital role. Read on to know if NLG is already deployed in your industry:
 

• Analytics Reporting

  Businesses across numerous industries use NLG to produce reports. Using NLG-powered Business Intelligence solutions, data can be analyzed to produce easily readable reports. A variety of complex graphs and data charts can be converted into understandable insights using a process known as Natural Language Reporting.

• Content Automation

  Another way NLG is used to automate content generation is by summarizing long content to produce personalized information. This technology can improve internal communications, product descriptions, agreements, company reports, contracts and other text-based communications. By automating manual writing, time and money is saved, while human employees can focus on other critical tasks.

• Virtual Assistants And Chatbots

  Digital assistants and chatbots that provide personalized, context-specific responses are one of the most effective applications of NLG. Well-known virtual assistants such as Alexa, Cortana, Siri and Google Assistant understand our inquiries, process the information and deliver the desired results, thanks to NLG!

• Improved Customer Relations

  Millions of customer interactions can be summarized and categorized using Natural Language Generation. Businesses can strengthen their customer relationships at scale by using personalized responses generated by NLG techniques based on the sentiment and mood of their audience.

You can bet that this diverse range of applications also brings home several benefits. Read on!
 

Benefits Of Natural Language Generation

 
Just as the NLG-powered virtual assistant helped Theodore from Her turn his life around, you can expect several advantages from NLG solutions – just make sure you don’t fall in love with them!
 

• High Speed Of Content Creation

  Text production time is slashed from minutes to milliseconds thanks to Natural Language Generation. Customer replies, sales reports and other content types can be created and used right away!

• High Accuracy

  Unlike human writers, NLG doesn’t have typos or writer’s block! Since NLG systems don't make spelling, grammar, or syntactical mistakes, the long-term cost of auditing every text has significantly decreased.

• Real-time Information

  NLG systems are capable of detecting and recording changes in real time. Then, based on the changes, NLG-powered assistants can create new narratives and reports to emphasize the modifications and alert stakeholders.

• Realigns The Workforce

  Human workers can engage in more imaginative and creative work that is beyond the scope of machines when they are liberated from tedious, routine work by NLG solutions. If an NLG chatbot can write an email about the drop in product sales, a human can analyze why it happened! #WinWin

 

What’s The Future Of Natural Language Generation?

Without a doubt, Natural Language Processing is advancing as a result of increased investment from tech behemoths like Google, Apple, Amazon and IBM. According to market intelligence experts, the global NLG market is projected to reach a size of USD 1.4 billion by 2027.

Our daily interactions with our devices will change because of NLP-powered intelligent systems. Everyday solutions will become more conversational and intuitive with NLG engines. It is also expected that NLG tools will be more frequently used to create text that will be almost identical to that written by humans. Soon, we might not even be aware that we're speaking with a computer and not a real person online! #TuringTest
 

Wrapping It Up

Natural Language Generation is a form of artificial intelligence (AI) that uses computer algorithms to generate human-like language from data. This technology can be used to automatically create natural-sounding text from structured data, such as financial reports, survey responses and customer service inquiries.

Wait, did you even realize that the above paragraph was written by an NLG bot and not a human writer? That’s Natural Language Generation for you!