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  • Writer's pictureFelix Laumann

Top NLP Trends and Predictions 2022: is NeuralSpace set up for the future of NLP?

Analytics Insight

Analytics Insight is a leading research institute in artificial intelligence, big data, analytics, robotics covering the latest trends in the industry. They recently published an article that describes the Top NLP Trends and Predictions 2022. NLP is on the rise and the advances are rapid, as mentioned in the article: “the global NLP industry is expected to reach US$42.04 billion by 2026, with a CAGR of 21.5%, according to Mordor Intelligence.”


NeuralSpace is a Natural Language Processing (NLP) company specializing in local languages spoken across Asia, the Middle East, and Africa. The NeuralSpace Platform is developers’ one-stop solution for any NLP features, does not require any machine learning expertise, and all that is needed is a handful of data to train your custom model with a click of a button.

Let us now unfold how the NeuralSpace Platform is well prepared for 2022 by already implementing the technologies and creating the possibilities that Analytics Insights mentions:

01. Transfer Learning

Transfer Learning uses the knowledge that a model has gained from being trained on one task to solve another task. At NeuralSpace we use it to train our Named Entity Recognition (NER) models by leveraging the intrinsic knowledge of a general-purpose language model and retraining it on a given set of entities. Transfer learning has helped us in extending our language support to more than 80 languages without needing much training data and time.

02. Fake News and Cyberbullying Detection

The amount of fake news and hateful or abusive phrases in user-generated content (UGC) has sharply increased over the last decade with ever-growing political discrepancies and populists fueling the trend even more. The NeuralSpace Platform has already been shown to be very effective in recognizing such content and achieved equally good results as AWS Comprehend, Google Vertex AI, and Hugging Face (read more). Detecting fake news and hate speech boils down to intent classification and entity recognition, which are combined in the Language Understanding app on the NeuralSpace Platform (try it out). Developers can train their own models in 87 languages with AutoNLP and do not need to worry about any model specifications — a simple click on the Train button in the corner of the Data Studio is enough.

Data preparation in the NeuralSpace Data Studio to detect hate speech in Arabic

03. Monitoring Social Media using NLP

Small and large brands from all over the world are eager to know how their customers speak about them on social media. Given the huge volume of such content, especially for the larger brands, this analysis must be automated. Developers typically repurpose intent classification models for this problem and detect the sentiment, or opinion, in social media posts by classifying intents as ‘positive’, ‘neutral’ and ‘negative’, sometimes also as emotions such as ‘angry’, ‘disappointed’, ‘satisfied’ or ‘excited’. As with the previous trend about fake news and cyberbullying detection, the Language Understanding app in the NeuralSpace Platform handles such cases at ease in 87 languages with AutoNLP. Developers define their training examples in the Data Studio with intents ‘angry’, ‘disappointed’, ‘satisfied’ or ‘excited’, click on the Train button, and have their sentiment analysis detector ready to be deployed.

Data preparation in the NeuralSpace Data Studio to detect hate speech in Hindi tweets

04. The use of Multilingual NLP will increase

The need for NLP solutions beyond English is known by many, yet the imbalance between English and non-English NLP models remains huge (read more). Given the increasing smartphone and Internet penetration in developing countries where local, or low-resource, languages are usually spoken, building more NLP models in these languages will become ever more important. At NeuralSpace we fill exactly this gap and build all of our apps in up to 87 languages natively. Developers can train their own models for language understanding, machine translation, transliteration, speech to text, and many more to come with AutoNLP and no requirements to understand the complex underlying deep learning models powering these apps.

05. Using a mix of Supervised and Unsupervised Machine Learning Techniques

At NeuralSpace we use transformers — large language models with millions of weights that are trained on text in multiple languages without any labels. We utilize transformers as base models and when developers want to build their language understanding solution, they finetune the transformer model in a supervised fashion, i.e., on training examples with labels. This has given us superior results on any kind of task in low-resource languages and a similar paradigm is also used in our speech-based NLP solutions.

06. Automating Customer Service: Tagging Tickets & New era of Chatbots

Chatbots have gained remarkable popularity during the ongoing pandemic and open-source frameworks like Rasa make it extremely easy to build your very own chatbot in a matter of minutes. The fundamental piece of AI that is in every chatbot is its ability to understand what the input sentence of a user actually means and what that user wants. The technical term for this ability is language understanding and we have come across this before with hate speech detection and sentiment analysis. This widely useable functionality is behind the Language Understanding app on the NeuralSpace Platform and is available in 87 languages. Many of these are spoken across Africa, the Middle East, and Asia, and include, for instance, 21 Arabic dialects, 11 local Indic languages, and many in Southeast Asia. NeuralSpace also has its direct connection to the open-source framework Rasa and developers can use Rasa how they are used to but NeuralSpace’s Language Understanding as the underlying language understanding functionality. This directly opens up Rasa to use in 87 languages with pre-trained language models.

07. The rise of Low-Code Tools

Let’s be honest, deep learning models are complicated and nobody really understands why an additional filter, layer, or gate actually leads to higher accuracy. Data scientists can easily spend months and thousands of dollars in computing costs to come up with a deep learning model that performs well on one specific task. At the same time, every company tries its best to automate as much as possible and gain insights into their data with deep learning. Inevitably, this leads to a rise in usage of low-code tools that do not require that data scientists define a full model architecture but rather focus on connecting the model to their product with APIs. The NeuralSpace Platform is a low-code tool that can be controlled through three modes: GUI via the web browser, APIs and CLI.

08. NLP will Necessitate a Comprehensive Strategy

According to Shubhangi Vashisth, Senior Principal Analyst at Gartner, “it takes about eight months to get an AI-based model integrated within a business workflow and for it to deliver tangible value” (read more). Machine learning operations, in short MLOps, reduces the time it takes to move AI models from pilot to production with a principled approach that can help ensure a high degree of success. The NeuralSpace Platform leverages its in-house AutoMLOps module that allows controlling versions, using the platform to deploy trained models at the click of a button and controlling the number of replicas to deploy. All developers need to do on the NeuralSpace Platform is to provide their dataset and their private model is trained for them with a simple click. Deploying the model for production use is also just a click away, thus reducing the time taken to go from experimentation to production drastically.

09. Transformers Will Lead the Way: BERT & ELMO

As we already explained in point 5 above, transformers are the base of nearly everything we do in text-level NLP solutions. However, it is important to understand that the fundamental idea behind the success of transformers, namely placing attention on certain words, can be utilized in speech models too. For those, NeuralSpace is building its own speech base models, which each user can fine-tune with labeled training data for their own project.

In the future, NeuralSpace anticipates becoming the leader in NLP for low-resource languages spoken across Africa, the Middle East, and Asia. In these geographies, local languages are spoken by more than three billion people and there is an ever-increasing need to provide technology that supports these languages natively. To know more about NeuralSpace check out our documentation and join our Slack community to stay updated!

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