Combining Generative and Discriminative Models in NLP

Natural Language Processing (NLP) is an ever-evolving field of computer science that focuses on the interaction between humans and computers using natural language. NLP encompasses various tasks, such as text classification, sentiment analysis, machine translation, and language generation. Researchers have developed several models to address these tasks, with two primary models in NLP being generative and discriminative models. In this blog post, I'll explain how combining generative and discriminative models can lead to highly accurate and robust NLP systems. Generative models are probabilistic models that can generate new text based on the input given to them. These models are trained on large amounts of unlabeled data and can be fine-tuned to perform various NLP tasks.