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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.

A brief guide to mobile app translation

Working on a mobile app and want to have it translated? Here’s what you need to know to get started. 

Did you know that 84% of the world’s population owns a smartphone? Smartphones have transformed our lives, and a large part of that has come down to mobile apps that have been created to make our lives and customer and business interactions easier, more efficient, and more convenient. If you’ve got an app that you want to take global or reach a new target audience in a different language, mobile app translation or app localization is a feature and service you should look into. Don’t know where to start? Here are some tips to get started in translating your mobile app. 

Why app translation is important

Cartoon app store UIs – Photo by 200degrees on Pixabay – Caption –“Mobile applications are now a part of our daily lives. Apps are more likely to be used if they are in the native language of the user.”

Apps are used for just about everything now, with the average smartphone user using around 10 apps per day and around 30 apps per month. If you do business in any other language other than English, getting your app translated is an effective way to increase your business within a set market. A study by Distimo in 2012 found that translated apps saw a 128% increase in app downloads and a 26% increase in paid subscriptions for those apps that had them. Now, these stats from 2012, imagine how much more these numbers would be in 2022. 

While mobile app translation may sound as simple as translating content from one language to another, there is much more to consider. 

Can I just Google Translate my app?

You could, but there are many good reasons why you shouldn’t. While machine translation in and of itself isn’t a bad thing, it’s missing an essential part of any quality translation, which is the proofreading and copy editing that comes from a human translator. Machine translations cannot consider cultural nuances and can still make obvious grammatical errors that can ruin any good mobile application. 

Related: Why Google Translate Isn’t Effective Enough for Business

Mobile App Translation and Localisation 1

So, what should you do if you want to translate your mobile application effectively?

Application localization

Mobile app translation is a necessary part of entering a new language market. However, translation in and of itself is very limiting. This is where mobile app localization steps in. Localization transforms and translates your product so it carries the same meaning and tone in the language it’s being translated into. It considers the cultural, geographical region, beliefs, local regulatory standards, and values of the target area in the translation process. It’s an adaptive review of your product to make sure that every aspect of the platform is suited to its new region. 

Here are a few areas that are often included in app localization,

  • User interface and general design
  • The tone of content and general appearance
  • Customer support base and contacts
  • Multimedia translation and localization, including images, audio, fonts etc.

Tips for simplifying the translation process

Mobile App Translation and Localisation 2

Photo by Elf-Moondance on Pixabay – Caption – “If you’ve never localized an app before, here are some important factors to consider.”

The first step in translating any app is to know your target markets thoroughly. This includes knowing your target users and their behaviour, along with proper market research. It will also require a substantial amount of project management and setting clear goals and outcomes of what you expect from the translation of your app. Here are some tips and suggestions to get you started on your software translation.

Separate translatable resources


Resources such as text, images, audio etc, that have executable code should be outsourced. This makes it so that content can be changed efficiently without having to change the base executable code of the app.

Keywords and SEO

Just like with websites, keywords and SEO are important for apps too. When you’re localizing, you need to consider your keywords, too and determine what words will work best in the regions you’re looking to enter. This localization of keywords will give you better rankings in app stores. So be sure to perform local word searches and know who your competitors are, along with what words, tone, and strategies they’ve used. 

Related: Why website translation and multilingual SEO optimisation are important for your online business

Text expansion and/or contraction


Depending on what language you’re translating into, text expansion and contraction are necessary for apps since they’re often used on small-screened devices. For example, English to Mandarin Chinese contracts by up to 20-50%, while the opposite occurs from English to German, with the text expanding anywhere from 10-30%. Not taking these factors into account when translating your app can result in serious user interface issues.

App store optimization


If you’re looking for an additional reason to localize your mobile app, both Google Play and the iOS AppStore can detect if you’ve localized, which can increase your app’s ranking. Further, by optimizing your app for the app stores, you’re increasing your chances of your app being successful. For example, when people are looking for an app, the first thing they see is the app name, meaning it’s important to have an app name that is descriptive and attractive. Both app stores also allow for a short description following the name, so use this to increase your ranking in app store results.

Quality assurance testing is a must


Having a linguistic QA specialist do proper QA testing on your app after the localization process is essential for a seamless app launch. QA testing ensures that your app works on all devices and platforms and that the translation work you’ve put into your app is flawless. QA testing ensures a better ROI, especially since users are less likely to use and engage with a glitchy or buggy app.

Work with an app localization service

If this guide has shown you anything, it’s that there are a lot of considerations, extensive planning and research that are needed to go into a successfully translated mobile app. To get the best out of your mobile app investment, it’s important to work with a language translation technology company that can take you through the translation process.

Into23 offers multilingual translation services for mobile app localization, software localization services, website localization and more. With international experience and a specialization in Asian languages, Into23 can help you mitigate risks and increase your ROI when entering the global market on any translation project. Contact us today to find out how we can meet your translation and localisation requirements.