Improving Natural Language Understanding with Google's BERT and GPT Models via Transfer Learning

[nimrah-waqar]

In order to enhance natural language comprehension problems, this topic focuses on the use of transfer learning approaches, notably Google's BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) models. Members of the community might talk about the capabilities and design of these models as well as how they are used in real-world NLP tasks like sentiment analysis, text categorization, and language translation. Talks could also focus on ways to improve the performance of these pre-trained models by modifying and adjusting them to certain languages or domains.