Natural Language Generation Models: The Change in Business Operations
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Think about all the content that surrounds us daily. People and their devices create an enormous data infrastructure that is growing exponentially over the years. Where will it take us, and how do we derive value from the information flow?
The biggest complication is that data can’t explain itself. It is basically useless without an expert who understands it. At the same time, the number of interconnected devices is already higher than the human population — we can’t process every data piece. But technologies can.
According to the Gartner predictions, data literacy will become an essential driver of business development by 2023. So right now is the moment to start managing your data properly.
Today, we’re uncovering natural language generation with Olga Kanishcheva, NLP Software Engineer at CHI Software. What NLG is and how it benefits the business world — our short guide explains it all.
What Is Natural Language Generation (NLG)?
Natural language generation is a subfield of artificial intelligence (AI) and natural language processing (NLP) that transcribes data into text and makes it understandable.
This technology has numerous applications in the business sphere, e.g., chatbots for customer support, answering questions by Siri and Alexa, or extensive reporting.
What is the goal of natural language generation?
Ideas for business optimization have always emerged from the information flows. But the amount of data is growing, and so is the need to keep up with the competition and improve customer service. In such circumstances, businesses turn to innovations.
The main goal of NLG is to derive ideas from any amount of data with the highest pace and accuracy. This task is particularly hard (if not impossible) for organization employees. NLG removes this issue altogether, as it can articulate human languages.
What are the key industry insights?
Natural language generation is a promising land for businesses, and statistics prove that. According to ReportLinker, the NLG market was estimated at 469.9 million USD in 2020 and is expected to reach 1.6 billion USD by 2027 with a CAGR of 18.8%.
The Chinese market will grow even faster — with a 24.1% CAGR. Other notable markets involve Japan, Germany, and Canada — they will grow at 13.6%, 14.8%, and 16.6% respectively.
Recent industry developments include:
- AX Semantics launched a content creation solution globally in December 2019. It helps companies produce listing descriptions in several languages. Among the clients of AX Semantics, you’ll find Deloitte, Nestle, Porsche, and other businesses from the e-commerce, finance, and media publishing spheres.
- Yseop, the world-known AI company, launched Augmented Analyst in February 2020. This NLG-based solution was created to help financial institutions with report generation and, as a result, boost digital transformation in the financial industry.
- Google presented BLEURT (Bilingual Evaluation Understudy with Representations from Transformers) in May 2020. It is a new technology for evaluating information, based on the BLEU automatic metrics and the BERT NLP technique. This combination forms advanced automatic metrics that can deliver human-like ratings.
Business Use Cases of Natural Language Generation
Since natural language generation is focused on creating understandable insights, it can be applied to any niche that deals with content creation, personalization, or reporting. Here are several most common NLG use cases.
1. Chatbots and virtual assistants
The time has passed when virtual assistants could give only short responses to queries. With the NLG techniques, Siri, Alexa, and Google Assistant compose answers with complex sentences similar to natural human speech.
Conversational AI technologies in general are great contributors to business development. In 2021, 62% of marketers considered voice assistance to be a ‘significantly’ or ‘extremely’ important marketing channel.
Moreover, generating sentences is not a limit to NLG capabilities. Natural language generation algorithms can produce a code that instructs a text-to-speech (TTS) engine to give more human-like responses.
Potentially, conversational AI will be able to express different emotions (for example, sympathy or excitement) using tags for emotionality. Chatbots have never been so close to human customer service agents.
2. Automated customer personalization
You won’t surprise customers by addressing them by their names via email. But natural language generation can help you go further. The only limit to modern technologies is the amount of customer data you have on hand.
For example, the Orlando Magic app generates text for custom messages and emails by adding personalized information. The in-built AI engine generates unique letters based on the usage history and tells fans how to get the most out of the app’s loyalty program. This innovation resulted in an 80% positive email response.
The same technology can add a pinch of personalization to all conversation channels: chatbots, in-app notifications, IVR systems, etc.
3. Content creation
Today, machines can’t replace human creative writing, but they already can augment it. For instance, Google’s Smart Compose gives you hints for writing emails.
But when it comes to data-heavy texts, such as product or meta descriptions, natural language generation models can already work independently. In 2020, the Babyshop group invested in NLG to create product descriptions with SEO customization on the company’s four websites.
The A/B tests results showed that NLG content generates as much or even more traffic than texts created by copywriters.
4. Inventory monitoring
Natural language generation models can also benefit industrial companies. After connecting to the IIoT (Industrial Internet of Things) infrastructure, NLG produces human-like updates on the inventory status, maintenance, and other points.
NarrativeWave provides solutions for industrial purposes to help businesses cope with big data and process false positive alarms. Natural language generation is a part of this system — it provides business insights that contribute to human decision making. This novelty can potentially save up to millions of dollars for big enterprises.
5. Reporting
Finalizing reports is one of the most tedious tasks for any manager or analyst, which, at the same time, requires an eye for detail. Natural language generation can take over this issue by providing highly accurate comprehensive reporting close to human writing.
By the way, reports concern not only charts and figures. It’s also applicable to the so-called “robot journalism” in weather reporting, sports, or financial news. Technologies help summarize information, while professional writers can focus on content-rich materials.
In 2018, Associated Press wrote more than 5,000 sports previews using technology only. This number is overwhelming for journalists but not for advanced algorithms.
NLG technologies have become more powerful than ever with the latest updates launched by OpenAI. We’re reviewing three GPT versions and their potential impact on the tech world. Continue reading in full.
