Author: Zen and the Art of Computer Programming
In recent years, with the rapid growth of large-scale text data, the increase in the amount of training data, and the popularity of deep neural network models, deep learning models in the field of natural language processing have also made great progress. Limited by the scarcity of real-world datasets, deep learning models often face the problem of overfitting. Therefore, how to use a small amount of labeled data to improve the generalization performance of the model has become an important topic.
The semi-supervised learning (Semi-Supervised Learning, SSL) method is a method to effectively improve the generalization ability of the model by using a small amount of labeled data and more unlabeled data. One of the main features of SSL is that it does not require a large amount of labeled data, but can use a small amount of labeled data to train a better model and fine-tune it to obtain better results.
This article will introduce the application of SSL in the NLP field in detail based on the pre-training task of the BERT (Bidirectional Encoder Representations from Transformers) model. BERT is a pre-training model based on the Transformer encoder structure introduced by Google, which is widely used in many tasks in the NLP field.
2. Explanation of basic concepts and terms
2.1 SSL
SSL refers to improving the generalization ability of the model by using a small amount of labeled data and more unlabeled data. Commonly used SSL methods include semi-supervised learning method, weakly supervised learning method, cross-entropy loss function method, masking mechanism, etc. Several of the most popular SSL methods will be covered here.
2.1.1 Unsupervised Learning
Unsupervised learning is a branch of machine learning that aims to allow computers to discover hidden patterns or structures in data on their own. Unsupervised learning has a wide range of applications, including image processing, bioinformatics analysis, text analysis, recommendation systems, etc.
2.1.2 Semi-supervised learning
In a real environment, we usually have a large amount of data that needs to be labeled. But the reality is often incomplete, more than