Natural Language Processing (NLP) offers a plethora of use cases that your business can benefit. As a cutting-edge research field, methods to improve NLP models and use cases are constantly being developed. If you come across to one of those methods called data augmentation, you may hear many positive comments about it but resources to understand what this method actually does may still sound too technical.
In this article, we will break down this technical concept into a simple definition, how it can be beneficial for NLP use cases and two main methods that it can be applied for your business.
Understanding how NLP data augmentation is applied:
Machine learning models are very data-intensive. A model that predicts which customers are likely to churn next month only by looking at 10 customers may not be any better than a guessing game. A model that looks at thousands of customers will start identifying patterns in the behavior of churning customers. Now let’s consider an NLP model that needs to decide which sentence to come after the other one. If you think about how rich human languages are, this task is particularly challenging for NLP models, because they will need to hear all possible ways a sentence is structured or a word is used in order to master the NLP task.
Data augmentation is the process of enriching or reproducing the data set that a machine learning model is trained on. With data augmentation, a bigger data set can be fed into the model to improve its output. This has been a concept that is widely used for image recognition and led to breakthrough applications such as brain imaging for early tumor diagnosis in health care. Following image recognition, data augmentation is now being used for natural language processing as well.
Top 3 benefits of data augmentation for NLP:
- Increase the accuracy of your models: A key success factor for many machine learning applications is accuracy. Recent academic studies and expert opinions show that data augmentation is one of the most promising methods to improve generative NLP models as complex as GPT models. If you have a business case that requires text generation, such as chatbots or autocompletion, then your NLP solution will benefit from data augmentation significantly.
- Adopt more advanced models: When it comes to decision making for a technical tool or analysis, you may often hear “we don’t have the data for that.” Data augmentation is a method to overcome this barrier for NLP applications. A comprehensive study between Google Research and multiple universities suggests that data augmentation is an enabler especially for large-scale NLP models that are otherwise challenged for data scarcity.
- Reduce bias in your models: A challenge with machine learning models, including NLP, is inherited bias in algorithms. This is often caused by the misrepresentation in data that the model is trained on. As an NLP example, it was discovered in 2010 that Amazon’s AI models that screened resumes treated women applicants unfairly, because there were less women in tech roles that the model was screening the good candidates for. Recent studies co-led by IBM and universities showed that data augmentation may eliminate the bias in NLP models significantly.
Selecting right tools for NLP data augmentation:
The simplest idea to tackle insufficient data is to bring more data. NLP use cases are specifically lucky on that, because web data is a growing source of human-generated text. If you have in-house NLP algorithms or leverage an NLP solution for your business, you can support your solution with scraped data from the web to improve your model.
Key benefit of this method is that the data being organic. Compared to the alternative method we will explain in a bit, scraped data from the web will improve NLP models with new and human-generated text.
Key challenge of this method is that the process of scraping can get long, especially if done in-house, because the websites being scraped will try to block you. Moreover, cleaning and preparing the scraped data for your model can slow down your process.
Read our in-depth guide on web scraping best practices to learn how to deal with web scraping challenges.
Another common method for data augmentation is reformatting the existing data and feeding it into the model along with the original data. There are several methods for that. One unique method is the process called back translation. In this method the algorithm will translate the text to another language and then translate it back through a software, which produces a different output from the original with almost the same meaning. Other methods are manipulating the text in several ways with protecting the meaning in general, such as deleting random words or replacing words with their synonyms.
Key benefit of this method is that it is a faster and easier solution to implement compared to web scraping since it will not have the scraping and data cleaning steps.
Key challenge of this method is that in the long run, it is not a sustainable way to improve your models. Unlike web scraping, manipulated data output will not be as different from your original data and the changes you can apply on the original data will be limited.
If you would like to learn more about how NLP can be applied for your business, its best practices and challenges, check out our posts below:
- Natural Language Understanding in 2022: in-Depth Guide
- 4 Challenges of Deep Learning & How to Overcome Them
If you believe that your business may benefit from a web scraping solution, check our list of web crawlers to find the best vendor for you.
Also, don’t forget to check out our sortable/filterable list of NLP Services.
For guidance to choose the right tool, reach out to us:
This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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