5 NLP Trends to Watch in 2021
In this article, see five NLP trends to watch for in 2021 such as organizations taking a holistic approach, breakthroughs moving from research into production, and more.
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Although still in its infancy, 2020 has been a year of significant growth for Natural Language Processing (NLP). In fact, research from Gradient Flow found that even in the wake of the COVID-19 pandemic, 53% of technical leaders indicated their NLP budget was at least 10% higher compared to 2019, with 31% stating their budget was at least 30% higher than the previous year. This is quite significant, given most companies are experiencing a downturn in IT budgets, as companies adjusted their spending in response to the pandemic.
With the power to help streamline and even automate tasks across industries, from finance and healthcare to retail and sales, leaders are just beginning to reap the benefits of NLP. As the technology advances further and its value becomes more widely known, NLP can achieve outcomes from handling customer service queries to more mission-critical tasks, like detecting and preventing adverse drug events in a clinical setting. As NLP continues on its growth trajectory, here are some of the top trends to watch in 2021.
1. NLP Breakthroughs Move From Research Into Production
One of the reasons for the increased investment in NLP is that recent advances in deep learning and transfer learning are now moving from research to production. In a healthcare setting, for example, advancements in reading comprehension enable algorithms to extract facts from radiology, pathology, genomic, and lab reports as accurately as humans. We're only starting to integrate this capability into the clinical workflow so practitioners can benefit. But, despite it being early days, NLP is already being used to diagnose patients, match them to clinical trials, highlight high-risk situations, and enable faster drug discovery. This has the potential to lessen the impact of an overburdened healthcare system, and will only get better over time.
2. Winning Organizations Will Take a Holistic Approach to NLP
As important as technical talent is to implement and scale an NLP project, understanding how AI will work within a product from a business perspective is equally vital to success. The most accurate patient risk prediction model won’t help anyone if it’s not integrated into the clinical workflow and easily trusted and used by doctors. A system that analyzes SEC filings in real-time won’t make money if the trading team doesn’t use it regularly to make better trades. An e-discovery system has to be designed around how lawyers practice to go beyond providing search results to help win cases.
All disciplines within an organization need to understand the benefits of integrating AI, and how it will affect their job function. Failing to train and actively involve product managers, designers, marketers, and sales professionals in designing AI and NLP systems is why so many projects don’t work in practice. Keeping these initiatives locked within a data science team often amounts to sprinkling the ‘AI’ buzzword onto a business and hoping for the best. It’s the investment in time, education, and practice across the entire organization that will separate the success stories from the tech laggards in the coming year.
3. NLP Will Continue to Get More Accurate
More than 40% of all respondents from the aforementioned survey cited accuracy as the most important criteria they use to evaluate an NLP library. Accuracy refers to pre-trained models that get used in multi-stage pipelines in NLP libraries—usually evaluated against standard academic benchmarks. Accuracy is vital for highly regulated industries such as healthcare and finance, where even small misinterpretations can have big implications. New academic research is helping providers of NLP technology challenge the status quo, making it possible for users to apply new, highly accurate pre-trained models into production almost immediately. The state-of-the-art keeps quickly advancing, turning more and more use cases from aspirational to commercially viable.
4. Multilingual Offerings Will Further Democratize NLP
For the past few years, you were out of luck if you needed NLP support in languages other than English, Mandarin, or a handful of others. Thankfully, multilingual models are now gradually being made available to data scientists worldwide. Cloud providers now offer support for over a hundred languages, and even libraries such as Spark NLP now offer 46 languages and keep growing. With new research advances such as language-agnostic sentence embeddings, zero-shot learning, and the recent public availability of multilingual embeddings, this will become the norm. More access to code and availability of many languages evens the playing field globally, resulting in a more diverse and inclusive AI ecosystem.
5. Pre-Trained NLP Models Will Lower the Barrier to Entry
Running some of the most accurate and complex deep learning models in history has been reduced to a single line of code, such as Python’s NLU library, for example. Additionally, some NLP libraries provide official support for their published models, so that models and pipelines are regularly updated or replaced when a better algorithm, model, or embedding becomes available. To take ease-of-use and support a step further, just like AutoML, AutoNLP is right on the horizon. With the advent of NLP model hubs—by Hugging Face, TensorFlow, PyTorch, and others— thousands of free, pre-trained models are available. To help take the guesswork out of finding the right model for your use case, better-faceted search, curated suggestions, and smarter ranking of search results are coming to fruition, too. This will make it easier for NLP novices to get started and for skilled data scientists to work more quickly and effectively.
With improved accuracy, multilingual availability, and greater access to pre-trained models, NLP is poised to grow even more significantly in 2021. While enhancements to the technology are a great first step, the success of NLP projects lie in how organizations ready their business and teams for production. As these projects move from the research phase to real-world applications, the organizations that take a holistic approach to NLP adoption and deployment will be the winners next year.
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