What is the Natural Language Generation? By what process can a machine/computer/system produce natural language? What are the uses of NLG? And why is it important?
Google Bard or ChatGPT? Amazon or Google personal assistant? If someone wants to talk to a system (machine) and get directions or ask it to do things for them – even tell jokes – they have many choices. We live in an era where machines understand natural language (human language). They both understand our language (read and listen) and can produce (write and speak) like it.
Different technologies and technologies worked hand in hand so that humans could build systems to understand and produce natural language. But the most important knowledge that made NLG possible was artificial intelligence (AI) and its very important and practical sub-branch, machine learning.
This article is about natural language generation and answers the questions posed at the beginning.
Table of Contents
What is Natural Language Generation (NLG)?
It has become normal for you and me to interact with computers, smartphones, and smartwatches. It means that we work very easily and quickly with different systems, we give them data and receive the data and outputs we want from them. In other words, with the invention of the interactive operating system, human-machine interaction became possible. With the arrival of artificial intelligence, this interaction went further. One-way human-machine interaction turned into two-way machine-human interaction. Now the machine can also interact with the human and answer him with a language that is not a number (0 and 1) or a code.
When you ask Google Bard a question, that chatbot searches Google for the answer to the question. It reads all relevant content, understands it, and then delivers the answer to you in a language you understand. Google Bard goes through a process to analyze the data and produce the result of the analysis in natural language. So, natural language generation or NLG can be defined as follows (this definition is from the Marketing Artificial Intelligence Institute website):
Natural language generation (NLG) is the process of transforming data into natural language using artificial intelligence.
The meaning of natural language production was determined. Now these questions must be answered: Why is it important for a machine to be able to produce natural language? What are the uses of natural language production? In what areas or industries is NLG used other than making smart chatbots and virtual (voice) assistants like Alexa?
What are the uses of NLG?
When we talk about the applications of NLG, we are talking about different tools that are made with artificial intelligence, and the necessary technologies for natural language production that are used in them. Artificial intelligence-based tools that make natural language production possible are used in many different fields and industries. In other words, NLG is important and practical because, just like other AI applications and tools, it makes things simpler, faster, and more efficient.
Among the most important applications of natural language production (in addition to the use in chatbots and voice assistants), the following can be mentioned:
Natural language generation tools can be financial reports, taxes, annual sales, and any other report that is full of numbers and figures or is very technical and specialized, and managers and employees of different departments of a business need to read and understand it for strategic and important decisions. read and summarize in a comprehensible and simple manner;
NLG has revolutionized marketing automation and digital marketing. A digital marketer can get help and suggestions from natural language production to personalize and customize different emails and messages to customers. Also, chatbots that answer customer questions in the support or sales department are built with this technology;
NLG tools are useful in producing any type of content for publication in the real and virtual world (news, educational, shopping websites, social networks, academic articles, etc.). With the help of this technology, you can write product descriptions, summarize important news, and simplify educational content.
How does a machine generate natural language?
How does natural language production happen? How can a machine be taught to collect and analyze data and speak or write the result in human language? NLG is a 6-step process. Of course, an important point that should be mentioned before dealing with NLG steps is that natural language production is a subset of another specialized field called Natural Language Processing or NLP. Using artificial intelligence and machine learning, NLP specialists teach the machine natural language processing, which consists of two parts (natural language understanding and natural language production). (In the next section, natural language processing and its relation to NLG will be explained in detail.)
The 6 stages of natural language production are:
The first step: analyzing the input content
The system must first check and find out what the user wants from him. Does he give him a text of 3000 words to summarize for him or does he ask him to read an email or find the address of a place and tell him? So, the first step is to analyze the content that the user gives to the tool to determine what the final response should be.
The second step: understanding the input data
Once the final answer is determined, it’s time to fully understand the data the user has given the tool. In this step, the input data is interpreted and patterns are found in it. At this point, machine learning is very helpful. With the help of the Python programming language and its powerful libraries, experts create algorithms and train them to deliver a certain output from the input data.
The third step: determining the output structure
Now that it is known what the user wants from the content of the data tool and the final answer has been found from the analysis of the input data, the outline of the final answer is drawn. For example, if the user wants a summary of the most important points in a financial report; At this stage, it is clear that the final answer should also be in the form of a report that has an introduction and a conclusion that includes the most important numbers and figures of the original report.
The fourth step: output sentence
Whether the output is text or speech, it must have correct and understandable sentences for the user. In this stage, the spoken or written sentences that were extracted from the input data or made based on them are arranged in an orderly and meaningful way.
The fifth step: determining the grammatical structure of the output
At this stage, the extracted or constructed sentences are grammatically correct and perfect to be spoken or written completely in the natural language of the user.
The sixth step: presenting the output
In the last step, the output that is ready to be delivered is presented to the user in the format and in the form that the user wants, text or speech.
NLP vs. NLG
As mentioned, natural language generation is a subset of natural language processing. Natural language processing is a set of technologies and techniques that translate human language for the machine so that the machine can understand it and provide the information it needs to extract and make available to humans. Natural language processing is divided into two parts: Natural language understanding (NLU) and natural language generation (Natural Language Generation or NLG).
Understanding natural language is the first step. It can be said that it is understanding. First, the computer must understand what the human means so that it can answer. In other words, in this part, the human language is translated into the machine language so that the machine can read or hear the human text or speech. Natural language generation is the next step. At this stage, the system gives a written or oral answer, again in the user’s natural language, based on the data it has received and understood from the user in his language.
Natural language processing and its subcategories are each specialized and separate fields. Funds indeed have a lot in common, but they also have differences. Different models and techniques are used to understand natural language and generate natural language. The meaning of the model is the model with which the machine/computer/system is taught to analyze the data and deliver a specific output.