Natural Language Generation (NLG) uses Artificial Intelligence (AI) to produce written or spoken language using datasets. It's an innovative technology that allows businesses to create useful content, including lyrics, movie scripts, speeches, and dialogues.
In this blog post, you'll learn more about this technology, how it works, its benefits in business fields, and its uses.
What is Natural Language Generation (NLG)?
Natural Language Generation (NLG) is an AI subfield capable of producing and generating textual explanations, comparisons, and summaries of databases. It uses analytics results to express concepts faster than manual procedures. NLG technology aims to provide machines with a capacity similar to human beings, allowing them to produce coherent texts through prompts.
NGL helps business team members, like analysts, when automating daily work, such as reports based on data or financial wallets. Likewise, it provides an efficient manner to communicate the most important data intuitively with summarized and detailed explanations that the individual user can control and take advantage of regarding their needs.
Traditional NLG only translated data to text in the past, but now, modern analytics solutions use enterprise-grade Natural Language Generation technologies, retaining AI-enhanced analytics capabilities. The idea is that they can communicate critical concepts with conversational and expressive language, including hidden nuances and patterns, full explanations about things, and displaying key findings within data.
What is the History of Natural Language Generation?
Natural Language Generation dates back to the 1960s when researchers started to explore simple and basic 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. While effective for limited applications, such as generating forecasts from weather data, they lacked the dynamism and adaptability to handle complex and varied narratives.
Later, in the 1980s and 1990s, the progress in statistical approaches and Machine Learning techniques led to significant advances in NLG research, including probabilistic models that could generate more fluid and contextually appropriate text, which involved several NLG systems for specific domains.
With the advent of Deep Learning models, Natural Language Processing, and Neural Networks in the late 2000s, NLG improved its understanding of complex model linguistic structures to generate coherent and contextually relevant text. Today, this technology is present in different contexts, like chatbots, virtual assistants, automated report writing, and personalized content generation.
The evolution of Natural Language Generation and Natural Language Understanding (NLU) includes rule-based systems and Neural Network-based approaches, allowing for the generation of more sophisticated and human-like texts with commercial applications.
How Does Natural Language Generation (NLG) Work?
Let’s understand how NLG works through some of the initial steps. So, later, you can dive into more advanced techniques.
1. Content Determination. In this phase, NLG starts with structured data as input from a wide range of source content, including databases, spreadsheets, and other structured data. In this case, you should determine the relationships between the main topics in the source document.
2. Data interpretation. This stage is where Machine Learning (ML) and Language Models come in. When you use NLG, data goes through analysis and interpretation, where your software identifies patterns in the data based on its algorithmic training. Considering numerical data or other types of non-textual data, your software detects the data you feed it with and understands how it relates to the actual text.
3. Document Structuring. The data go through an organization process to create a narrative structure and a documental plan. To better understand it, we can mention football news as an example, which starts with a paragraph that indicates a game's outcome using a comment to describe the game's intensity and competitive level. Then, the writer considers the teams' pre-game classification, describes other aspects related to the game in the following paragraph, and concludes with interviews with players and coaches.
4. Sentence Aggregation. This function, also known as microplanning, involves selecting expressions and words for each sentence for the end user. In this stage, it applies sentences in different contexts, considering their relevance.
5. Grammaticalization. The grammaticalization stage guarantees that the entire report follows correct grammatical form, spelling, punctuation, and validation of the actual text according to syntax, morphology, and spelling rules.
6. Language Implementation. This stage integrates data into templates (reports, customer-directed emails, or voice assistant responses) to guarantee that the document is in the desired format according to user preferences.
Where to Start with Natural Language Generation?
Using Natural Language Generation systems requires thought and planning. In the next section, you can appreciate some preliminary considerations for adopting NLG models.
● Use Case. You must decide how frequently you will produce external or internal elements such as financial reports, summaries, fact sheets, etc. Furthermore, you should consider the narrative to be in a consistent and repeatable format.
● Structure. Regardless of the use case, Natural Language Generation needs structured data, and you must ask yourself: Are the data sets organized into ordered columns and rows? In this case, the data must be clean and relatively consistent to use this technology. Hence, it is important to invest time and resources in cleaning data before loading it into an NLG system.
● Feasibility. Even the most basic Natural Language Generation solutions require time to configure. To determine the feasibility of using NLG, you should analyze long reports, articles, or narratives and then see how much time you can save using NLG.
Understanding basic programming and Machine Learning concepts is important before using Natural Language Generation. This technology is useful for generating human-like text using algorithms, so familiarity with coding languages like Python or R can be beneficial. Furthermore, understanding statistical modeling and linguistic principles can aid in the creation of more accurate and coherent text.
NLG libraries and frameworks provide pre-built models so you can implement them more easily. Likewise, exploring and practicing with sample datasets to gain hands-on experience with NLG techniques would be helpful.
Natural Language Generation Applications
Using NLG considers numerous practical applications in various business domains; you may be experimenting with NLG daily and are unaware of it. Natural Language Generation has the potential of making sense of data and creating human-readable knowledge. It applies to areas such as reports, content creation, and personalization. Here are some business areas where NLG excels.
● NLG for Retail. NLG solutions enable you to create product descriptions and categorizations for online shopping and ecommerce, allowing you to personalize customer services by using bots within chat responses.
● NLG for Finances. The banking industry depends on data for performance reporting. For this reason, the profit and loss reporting should go through an automation process using NLG systems. In other words, NLG techniques support chatbots that interact with users to bring personal advice on financial management.
● NLG for Manufacturing. You can use NLG to automate the communication of critical findings, such as IoT device status and maintenance reports, allowing employees to act more quickly.
● NLG for Media. NLG assists you in resuming content creation in media, particularly sports and financial news. These stories typically follow similar templates and are simple to produce.
● NLG for Insurance. In this field, insurance NLG solutions can assist you in better communicating personalized plans to clients.
● NLG for Transportation. In this case, chatbots can send delays and schedule alerts, which, combined with NLG tools, allows for creating personalized, easy-to-read travel plans.
Some examples of NLG applications include chatbots, voice assistants, social media posts, machine translation tools, conversational AI assistants, analytics platforms, AI blog writers, sentiment analysis platforms, AI-powered transcription tools, and narrative generation tools. In addition, some companies that have used this technology in their business processes include GPT-3, LaMDA, Wu-Dao, and Smart Compose by Gmail.
Why is Natural Language Generation (NLG) Important?
The techniques of NLG are relevant today and can appear in various contexts, including daily sports news, actual forecasts, updates, and voice search features in search engines like Google. NLG can automate written content development, saving time and money, but it still needs human intervention. Personalization is another reason, considering NLG provides personalized material for specific users and improves User Experience (UX), such as creating customized automatic reports and emails.
It's no secret that NLG enables business scalability by producing massive amounts of content quickly, which would be hard to achieve manually. In the same order, consistency is another attribute that allows these complex models to consider style and voice, which are important for brand identity. Accessibility is important to content generation because NLG enables you to translate complex data or technical knowledge into easy and intelligible language.
Finally, we might include interaction, chatbots, and virtual assistants using Natural Language Generation to provide human-like responses, making them more natural, useful, and appealing. Using NLG can also generate real-time responses or updates, which can be valuable in various situations, such as customer service, social media monitoring, and financial trading.
Conclusion
The rise of NLG solutions and machines improving at generating texts 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 collaboration essential. Linguists may aid in deepening the insights into the aspects of language production, like syntax and semantics, of machine-generated text. At the same time, storytellers can ensure the presence of the true essence of data-driven narratives, and ethicists can direct these technologies' moral compass.
In the intersection of code and human language, computer-generated text, and human narratives lie the future of enhanced writing automation capabilities and, most importantly, stories to tell.
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