An increasing number of companies are tapping into Natural Language Processing (NLP) to optimize their services through chatbots, voice assistants, intelligent document processing, and other solutions. But while NLP techniques are improving, the data we collect to train NLP models still has a long way to go.
Humans perceive written or spoken language contextually, and machines do so logically. So to produce desired results, they need properly curated data – lots of it. The more high-quality data NLP models are trained on, the more advanced they become.
How to get quality data to train your NLP models? It all depends on how you manage your data collection. NLP covers several different data types: audio (Automated Speech Recognition or ASR), digital text, handwritten text (Optical Character Recognition or OCR), or text in images (OCR). Regardless of your selected data type, there are a few things you need to be aware of when it comes to collecting data.
Make sure the data you collect covers the real-world usage of the model. For example, if you have a speech recognition model trained only using British English speakers, it will not work well when used by American English speakers or non-natives.
Bear in mind the complexity and fluidity of language. NLP is rapidly progressing, but language is a moving element that evolves and presents new challenges. Your NLP models need to consider different age groups, genders, nationalities, accents, slang, and other factors that may play a role in how people speak.
By saying diverse data, we don't mean it has to cover data from every potential use case; instead, it should cover practical use cases. Most machine learning algorithms are narrow, meaning they have a limited use case they cover; the data should match that.
The more data you have, the better. However, it all comes down to how it is collected. For most algorithms, it is better to collect data from many people rather than just a few. Even if those few supplied large volumes of data. This is because having just a few people provide the data will not ensure enough variance to cover the entire sphere of usage.
A good way to extend existing datasets is using synthetic data. You can build a model to generate synthetic data independently or collaborate with a data vendor.
Synthetic data can be created using an algorithm, which will generate data similar to the data you already have. This method can be risky unless you have a process to ensure the data generated is accurate.
Another method to create synthetic data is having real people replicate specific scenarios. That way, synthetic data simulates required cases or conditions that aren’t represented in existing data and helps fill in the gaps in datasets.
Using synthetic data means that model training won’t contain any real information, alleviating sensitive data concerns. However, running NLP models on only synthetic data is not a common practice - it is challenging to mimic real data in an authentic way. This is why most NLP models use datasets that are around 80% real and 20% synthetic.
Web scraping is also gaining momentum as it is a great automated method to collect a lot of data over a short time. The web is undoubtedly the most significant data repository, but it also comes with certain risks.
In many cases, scraped data is illegal to use in commercial products due to copyright and privacy issues. When scraping data online, you need to be aware of websites’ terms of service, including which parts are off-limits, and follow data protection protocols. Which brings us to the next point.
Privacy regulations are one of the most crucial aspects to consider when collecting data to train NLP models. They can vary drastically from one location to another as different countries have different data laws. Collecting data from each country means you must follow that country's laws, making it even trickier to handle data collection independently.
You can also buy ready-made datasets from data providers. There are several sources available for NLP data in English; however, the more specific your needs are, the fewer providers and datasets are available.
Provider selection is scarce for datasets in other languages. If you cannot find any datasets that fit your needs, you can contact us at StageZero, and we will look into creating them for you.
Collecting and qualifying training data for NLP models is a complex undertaking that is time-consuming and resource-intensive. Meanwhile, working with third-party companies can be a hassle if they don't support the standard formats for a segment.
StageZero offers support for standard data formats but can customize the delivered data based on your needs. We are specialists in speech and text data creation for virtual assistants, chatbots, and other applications and can give you access to over 10 million users to provide needed data.
These users can also be used for text labeling of handwriting, math, drawings, and more. Text labeling can improve the functionality of chatbots or give new insights into sentiment analyses of, for example, your brand perception among your customers.
Reach out to us to hear more about the best data collection practices.