Introduction to Self-Supervised Learning in NLP


  • Introduction to Self-Supervised Learning in NLP


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Frequently Asked Questions

Machine learning for natural language processing and text analysis requires a set of statistical methods which will help them with the identification of speech, sentiments, entities, and other emotional parts of the text. The technique that is used for expressing it as a model which is then applied to other texts is known as supervised machine learning.

One of the well-known examples of a self-supervised learning model is speech recognition. For example, the application developed by Facebook wav2vec runs on the self-supervised learning model. It performs speech recognition with the help of two deep convolutional neural networks which are built on one another.

Language models are trained using a self-supervised model which are having tasks over huge amounts of unlabeled text. For example, when you take the masked language task, some token fractions in the original text will be masked randomly, and the language model will attempt in predicting the original text.

The self-supervised learning model is a representation where a supervised task is created with the help of unlabeled data. It is majorly used for reducing the data labeling cost and leveraging the unlabelled data pool.

Both self-supervised and unsupervised learning models are similar. The major difference among them is that the self-supervised learning model aims to tackle tasks that are traditionally done by supervised learning models.

The supervised learning models are of two types: regression and classification. Classification separates the data, whereas the regression fits the data in the required space.

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