The world is being taken by storm by “natural language generation” (NLG), as billion-dollar language startups are upending how humans interact with machines and create content. This transformative AI technology is different from other AI language technologies, and the Marketing AI Institute has spent years studying its impact on marketing. This article will provide everything you need to know about NLG.
What is natural language generation?
NLG is the process of using artificial intelligence to transform data into natural language. NLG software uses machine learning and deep learning models to convert numbers into natural language text or speech humans can understand. Chatbots, voice assistants, and AI blog writers are among the systems that utilize natural language generation. NLG systems can produce narratives based on pre-set templates and predict the words that need to be generated next, such as in an actively typed email. The most advanced NLG systems can formulate summaries, articles, or responses.
Difference between Natural Language Generation and Natural Language Processing:
NLG and Natural Language Processing (NLP) differ in their roles in generating human-like text or speech using artificial intelligence.
NLP is responsible for converting human language into a format that machines can understand. It reads or hears input from a user, such as spoken words or written text, and then processes it to identify the meaning behind the words. This includes parts-of-speech tagging, named entity recognition, and sentiment analysis.
On the other hand, NLG uses the processed data from NLP to generate text or speech that mimics natural language. It writes or speaks output in a way that a human would understand and respond to based on the input it received.
In summary, NLP focuses on understanding human language and translating it into machine-readable data, while NLG focuses on using that data to generate human-like responses.
Difference between national language generation and national language understanding:
NLG and NLU are two important aspects of natural language processing. NLG is responsible for generating language that sounds human by using the data produced by NLP. On the other hand, NLU uses computational models to interpret the meaning behind human language, analyzing the data produced by NLP to understand the meaning of the words and the relationships between concepts.
In simpler terms, NLG focuses on creating human-like language, while NLU focuses on understanding the meaning behind that language. Suppose the NLU successfully understands the meaning behind the language generated by the NLG. In that case, you get a response from a chatbot or voice assistant that makes sense and is easy to understand.
Applications of National Language Generation:
NLG technology finds numerous commercial applications, and people experience it almost every day, often without even realizing it. Various examples of advanced NLG applications include:
- Websites with chatbots that can answer questions automatically.
- Voice assistants such as Alexa or Siri can respond to voice commands.
- Machine translation tools that translate one language into another.
- Conversational AI assistants that use advanced NLG and NLU to hold two-way conversations.
- analytics platforms that can use NLG to explain insights from data in simple language.
- AI blog writers for content creation use a language model to generate anything from a sentence to an entire article automatically.
- Sentiment analysis platforms that use NLU to understand which language resonates with customers and use NLG to create messages they are likely to respond to.
- AI-powered transcription tools that use speech recognition to understand audio and NLG to turn it into text.
- Narrative generation tools that use structured data (often in a spreadsheet) automatically generate a text narrative.
- NLG is employed extensively in various aspects of business and consumer life. Let us examine some specific companies that develop NLG for these use cases.
National Language Generation Tools
Numerous Natural Language Generation (NLG) tools natural language generation for commercial writing and speaking purposes. The Marketing AI Institute keeps tabs on thousands of AI vendors and has compiled a list of some of the best NLG tools available:
- Arria extracts and summarizes data into easily readable narratives using NLG.
- Automated Insights uses NLG to create earnings reports, sports articles, and data-driven narratives on a large scale.
- Clickvoyant leverages NLG to reduce the time taken to extract insights from analytics and create presentations based on those insights.
- Drift uses conversational NLG through chat, email, video, and automation products to streamline the buying process.
- Exceed.ai engages with sales leads via human-like, two-way conversations through email and chat using AI.
- HyperWrite automatically generates sentences and paragraphs based on human prompts, making it an excellent NLG tool.
- MarketMuse employs NLG to assist with content strategy and creation by automatically generating summarized briefs and even text.
- Narrative science uses NLG to convert structured data (spreadsheets) into human-like narratives.
How to get started with National Language Generation
To get started with natural language generation in business and marketing, you need to do some planning and consider the following steps:
- Determine if you have a use case for basic NLG by looking at the stories you already manually tell with numbers. Consider whether you produce these stories regularly and if any of them are in consistent, repeatable formats. Low-hanging fruit can create value faster and teach you the basics of NLG. PR 20/20, the marketing agency behind the Marketing Artificial Intelligence Institute, used NLG to reduce the analysis and production time of Google Analytics reports by 80%.
- Look at how your data is structured. NLG needs structured data to work, so check if your datasets are organized in ordered columns and rows. Clean and consistent data is necessary for uploading to an NLG system.
- Be realistic about your ROI. NLG solutions require substantial time to set up and pay for, so analyze how long your current reports, articles, or narratives take and see how much time NLG can save. Apply those time savings to all staff that NLG would affect to determine if it makes financial sense for your organization.
Conclusion
Natural Language Generation (NLG) has emerged as a transformative technology in recent years, and it is poised to continue revolutionizing various industries. NLG has become a valuable tool for generating written or spoken language that mimics human-like communication. From automating content creation to personalized messaging and chatbots, NLG has proven its value in enhancing the user experience and streamlining various business operations.
As NLG technology advances, we can expect to see even more sophisticated applications that provide a more seamless and natural interaction between humans and machines. With the potential to improve communication, reduce errors, and increase efficiency, NLG is a promising technology with many applications in healthcare, finance, marketing, and customer service.
