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NLP in 2021: 6 trends to watch out for

From chatbots to translators and many similar tools, Natural Language Processing (NLP) as a technology is integral in our everyday lives. It is, along with text analytics, fast maturing and is now able to comprehend the ambiguities, nuances and context within human language. 

Thanks to the growing interest in human-to-machine interactions, easy access to Big Data, an ever-increasing volume of unstructured data, powerful computing and more, the technology is growing quickly.

According to a survey by Gradient Flow, 53% of respondents (technical leaders) said their NLP budgets were at least 10 percent higher in 2020 compared to the previous year. It is evidence of the increased adoption of NLP, which can help organizations streamline business processes and automate mundane tasks.

So, what is in store for natural language processing in 2021? Let’s find out. 

Multilingual NLP to grow

Over the last few years, you didn’t have much of an option if you required NLP support in languages apart from English, Mandarins and a few others. 

Now, though, multilingual NLP models are becoming available to data scientists the world over, with cloud providers supporting more than a hundred languages. (Libraries like Spark NLP support 46 languages, and that number is growing.)

With recent advances like language-agnostic sentence embeddings, public access to multilingual embeddings and more, open-source libraries are following in the footsteps of the existing cloud providers. Therefore, in 2021, you can expect multi-language support in NLP becoming the norm. 

Improved customer service

Across industries, 2020 was a tough year for customer service given the increase in support tickets. Increase in the volume of support tickets made it challenging for organizations to respond quickly to urgent queries. 

With advancements in NLP and increasing demand of customer service, next-gen chatbots are expected to play a key role as they self-improve, become capable of holding complex conversations and learn to accomplish new tasks with minimal or no prior training. This in turn is expected to free customer support agents of tedious, time-consuming tasks (tagging and routing support tickets being one) and allowing them to focus on higher-value tasks. 

Customization experience

Over the coming months, customization experience using NLP will be one of the major focus areas as global brands continue to integrate NLP across their business pipeline.

The caveat for organizations, though, is that to achieve high accuracy within their respective domain, they must add a layer customization for text classification and sentiment analysis. Training a machine learning model is essential to achieve the desired accuracy. 

Increased accuracy

A new academic research is enabling NLP technology providers to disrupt the status quo. Thus, it’s becoming possible for users to apply pre-trained models with high accuracy into production almost instantly. 

For example, according to the aforementioned survey by Gradient Flow, 40 percent of the respondents said mentioned accuracy as the most important criterion in evaluating an NLP library. Accuracy refers to the use of pre-trained models in pipelines in NLP libraries, typically gauged against standard academic scales.  

Accuracy is a non-negotiable in industries like finance, healthcare and others where even the slightest differences can have major implications. With advancements in NLP, an increasing number of use cases are becoming commercially feasible. 

Using NLP to improve social media monitoring

Sentiment analysis, also known as opinion mining, will continue to play a key role in brands understanding customer sentiment and addressing their grievances. 

Therefore, NLP tools remain essential to brands being able to monitor social media conversations and gather real-time insights into customer feedback and prospects’ feelings about their products and services. 

In addition to helping brands gain a competitive advantage, sentiment analysis is capable of analyzing the efficiency of marketing campaigns and also measuring how customers react to company announcements involving product release, addition of product feature and more. 

Fake news and cyber bullying detection

Rapid advancements in technology for online communications via social media channels have contributed to an increase in the spread of fake news and misinformation. 

Last year, amid the alarming scale of false information surrounding the pandemic, some interesting approaches were made towards automatic detection of fake news (the use of transformers was one approach). 

Natural language processing is helping reduce the time and human effort required to detect and stop the spread of fake news. Classifiers, for instance, are being leveraged to identify offensive or insulting language, or hate speech online. 

Given the current debate around whether social media conversations should be regulated, these applications of NLP may become more relevant. 


With increased accuracy, multilingual capabilities, identification of fake news and cyberbullying, and more, NLP is poised for significant growth in the year 2021. As improvements to the technology provide the foundation, the key to the success of NLP projects depends the ability of organizations to prepare their teams and business for production.

Author Bio: Suhith Kumar is a digital marketer working with Indium Software. Suhith writes and is an active participant in conversations on technology. When he’s not writing, he’s exploring the latest developments in the tech world.

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