What is Natural Language Generation (NLG)?

Did you know that Natural Language Generation (NLG) is behind machines being able to produce written or spoken content?

This field of Artificial Intelligence (AI) leverages datasets to create content such as lyrics, movie scripts and speeches.

In this blog post, we'll learn more about NLG, how it works and its benefits and uses for businesses. Let's start! 

What is Natural Language Generation

Natural Language Generation (NLG) is an AI subfield capable of producing and generating textual explanations, comparisons and summaries.

I relies on datasets and analytics results to express concepts, often faster than manual processes.

NLG technology aims to provide machines with the capacity of producing coherent texts through prompts. 

Likewise, it provides an efficient manner to communicate the most important data intuitively with summarized and detailed explanations.

With this information, users and teams can control data and take advantage of it within specific scopes.

In its beginning, NLG only translated data to text, yet currently its analytics capabilities have expanded.

Today, NLG allows businesses to translate data into conversational and expressive language, including hidden nuances and patterns.

The History of Natural Language Generation

Natural Language Generation dates back to the 1960s!

Back then, researchers started to explore simple techniques to generate automatic texts using structured data.

The first efforts in NLG incorporated rules and used pre-defined templates, including grammatical rules for generating text. 

These template-based systems had fixed textual structures, completed with specific details.

At first, they were effective for limited applications, such as generating forecasts from weather data.

Yet, they lacked the dynamism and adaptability to handle complex and varied narratives.

In the 80s and 90s, progress in statistical approaches and Machine Learning techniques led to advances in NLG.

Some examples here included probabilistic models that could generate more fluid and contextually appropriate text.

The 2000s saw an advent of Deep Learning, Natural Language Processing, and Neural Networks.

With this advances, NLG improved its understanding of complex linguistic structures to generate coherent and contextually relevant text.

Today, the NLG technology is present in contexts, like chatbots, virtual assistants and automated report writing.

The evolution of Natural Language Generation includes rule-based systems and Neural Network-based approaches.

As a result, NLG can generate more sophisticated and human-like texts with commercial applications. 

How Does Natural Language Generation Work

Let’s see how NLG works through some of its initial steps.

1. Content Determination. NLG starts with inputs of structured data and teams must determine the relationships between topics.
2. Data Interpretation. With Machine Learning and Language Models, data is analyzed to find patterns between texts and non-textual data.
3. Document Structuring. After analysis, data go through an organization process to create a narrative structure and a documental plan.
4. Sentence Aggregation. Also known as microplanning, this stage selects expressions and words considering end users, contexts and relevance.
5. Grammaticalization. The grammaticalization stage guarantees correct forms, spelling, punctuation, syntax and morphology.
6. Language Implementation. This stage integrates data into templates (e.g.: reports, emails or voice responses) to enforce the desired formats. 

Where to Start with Natural Language Generation

Using Natural Language Generation systems requires thought and planning!

We'll now see some preliminary considerations for adopting NLG models. 

• Use Case. Teams must decide how frequently they'll produce outcomes to ensure consistent and repeatable formats.
• Structure. Data must be clean and relatively consistent—invest in optimizing data before loading it into an NLG system.
• Feasibility. To determine NLG feasibility of using , analyze current formats to see how much time you can save using NLG.
• Knowledge. Understanding basic programming and Machine Learning is also important before using Natural Language Generation.
• Toolkit. Knowing Python or R, as well as statistical modeling and linguistic principles, can help building accurate and coherent outcomes. 
• Testing. Practicing with sample datasets to gain hands-on experience with NLG techniques can be quite helpful! 

Natural Language Generation Applications

Natural Language Generation helps make sense of data and creates human-readable knowledge in several fields.

You may be leveraging NLG daily without even knowing it! Here are some business areas where NLG excels. 

• NLG for Retail. NLG help creating product descriptions and categorizations for online shopping and eCommerce and personalize customer services.
• NLG for Finance. The banking industry leverages NLG systems for profit and loss reporting, as well as personal advice on financial management. 
• NLG for Manufacturing. NLG can automate the communication of IoT device status and maintenance reports for employees to act faster.
• NLG for Media. NLG assists in content creation in media, particularly in categories that follow similar templates and are simple to produce.
• NLG for Insurance. NLG-driven solutions can help the insurance industry better communicate personalized plans.
• NLG for Transportation. Chatbots can send alerts that, combined with NLG, allows for creating personalized, easy-to-read plans.

Examples of NLG applications include chatbots, voice assistants, social media posts and machine translation tools.

It can also be seen in Conversational AI assistants, analytics platforms, sentiment analysis platforms and AI-powered transcription.

Some companies leveraging NLG in business operations include GPT-3, LaMDA, Wu-Dao and Smart Compose by Gmail. 

Why is Natural Language Generation Important

NLG is key for daily aspects of life, like news, forecasts, updates and voice search features in search engines.

While it can automate content development, saving time and money, Natural Language Generation still needs human intervention.

Personalization is another reason when weighing in that NLG provides personalized materials helping improve User Experiences.

What's more, NLG can be key in business scalability as it produces amounts of quality content that would be hard to achieve manually.

Consistency is another attribute that allows NLG to consider style and voice, which are important for brand identity.

With NLG, companies can also focus on accessibility and translate complex data into easy and intelligible language.

In this context, virtual assistants using NLG provide human-like responses, which enhances appeal while catering to accessibility.

Using NLG can also generate real-time response, which can be valuable in customer service, social media monitoring, and financial trading.

Conclusion

NLG represents a societal change and a standard feature to power BI (Business Intelligence) and analytics platforms.

However, as we embrace this future, we must consider human-machine collaboration essential.

Linguists may aid in deepening syntax and semantics in machine-generated language production.

At the same time, storytellers can ensure the presence of the true essence of data-driven narratives.

In the intersection of code and human narratives lies the future of enhanced capabilities and, most importantly, stories to tell.

If you want to leverage NLG to build the stories users love, contact Capicua!