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What is NLG and how does it work with the Natural Language Process (NLP)?

NLG is a type of AI and language translation technology that is becoming more prominent in business platforms.

NLG standards for Natural Language Generation and it is changing the way we interact with machines and the way businesses gather data. What is NLG exactly, and what makes it different from other technologies? With the compound annual growth rate of the NLG market expected to reach 1.6 billion dollars by 2027, you need to know about NLG.

What is NLG?

Chatbot

A chatbot, which is often used for FAQ portions of websites and customer support, is one type of NLG. – Photo by mohamed_hassan on Pixabay

NLG is a type of AI that automatically processes data into sentences and stories, in either written or narrative form, in a way that’s easy for us humans to understand. The NLG can take massive amounts of data from pre-set templates to form a sentence, reply, or inquiry that reads like a natural human conversation. This data and our inputted responses to this data create and add to a database of information that businesses and researchers can use to improve a process or product. 

When is NLG used?

Alexa

Any time you beckon Alexa or Siri, an NLG has been used to create that product and experience – Photo by Anete Lusina from Pexels

NLG is being used for a vast array of applications, and chances are that you’re already encountering and engaging with this technology daily. Here are a few broad ways that both businesses and consumers use NLG,

  • Chatbots or conversational AI assistants – Used on websites and business platforms to automatically answer customer inquiries. The advanced use of NLG carries a two-way conversation and responds to verbal commands. Examples are platforms Alexa, Siri, Google Assistant and Cortana. 
  • Machine translation tools – Tools that translate one language to another, such as Google Translate.
  • AI blog writers – Used for content creation. The NLG uses one language model and data set to write sentences and full-length articles. 
  • Analytics – The NLG is created to detail insights from business data and reports, such as financial reports and spreadsheets and put it all into a format or narrative that’s easy to understand for businesses and their customers. 
  • Automated leading emails and messages – This NLG creates predictive text for users when writing in emails and messaging platforms. 
  • AI transcription tools – With speech recognition, the NLG takes audio and turns it into text.
  • Semantic analysis A platform used to determine what language best resonates and reaches a specific audience of which the NLG is used to create messages to which the customer is likely to respond.  

NLG has become one of the translation solutions used by global businesses as part of their website localization, eLearning translation and more. NLG is used as part of the machine translation post-editing process used by international translation companies and translation agencies online

How the Natural Language Process (NLP) works

Computer AI

NLP is a series of Ais that work in a relationship with a user to create and exchange of information that benefits the user and the business.. – Photo by geralt on Pixabay

 

NLP is a blanket term that refers to NLG and Natural Language Understanding (NLU). NLP is a framework that converts unstructured data to structured data. NLU is the ability of a machine to use syntactic and semantic analysis to gather meaning from a piece of text or speech. It is the NLG that allows devices to create content from the NLU data content. In short, NLU lets a computer understand what data the user is giving it. At the same time, NLG provides data back to the user from the computer in a way the user can understand, thus the Natural Language Process.

Making an NLG requires several steps and a substantial amount of NLU data to create content that resonates and sounds natural. Whether it’s a chatbot or a machine translation tool, these are some of the steps and considerations that go into making an NLG,

  • Content analysis – This step analyses data to identify the main topics that should be included and what the result of the process is expected to be.  
  • Data interpretation – As patterns are identified, they are put into context for machine learning.
  • Document structure and sentence aggregation A document is created along with a narrative structure with the interpreted data. From there, relevant sentences are isolated and combined to depict the topic accurately. 
  • Grammar – Grammar rules, along with the syntactical structure of the source language, are added to ensure that the text sounds natural.
  • Final output – The final output is presented in a template or format of the programmer’s selection. Such as a message from a chatbot, a piece of the translated text, an audio reply for personal assistant devices etc. 

The Future of NLG

Woman on phone transit

NLG is used in a variety of apps and processes on our phones. – Photo by Ketut Subiyanto from Pexels

NLG has created ways for businesses to communicate data efficiently and effectively, which increases productivity and reduces business costs. It presents data and information in an accessible manner while collecting big data that will lead to specific insights into a business. NLG has been used in different business industries, from insurance, retail, finance, media, eLearning platforms, eCommerce and eCommerce translation, manufacturing, translation management and more. 

While technology has come a long way, NLG is still limited compared to real human writing and semantics. NLG can only act on the NLU data, which, currently, doesn’t stack up to the ingenuity of human writing and content, which makes the quality of NLG content one of its biggest weak points. NLG, however, is not without its merit as the NLP is superb at generating human insights from big data, especially at a volume that we, as humans, are not capable of producing. As NLG can be used in various markets, it is a valuable tool that can be used in many ways for any business. Take translation and localization, for example.

For businesses that want translation and localization services to expand into other global markets, NLG is an important part of a quality translation. Translators use machines to help expedite the translation process and fine-tune it with their human expertise. This process is called machine translation post-editing.

Related: Machine, mind, or machine and mind: how to best deploy today’s machine translation solutions

Into23 provides translation management and translation solutions that cater to your business. Into23 can help you use an NLG in multiple languages for your business; whether it’s a customer support chatbot or transcription services for a voice assistant, Into23 can help your customers interact with your business better.