GTLNLP: A Mathematical Exploration of Cross-Domain Knowledge Transfer for Text Generation for Generative Transfer Learning in Natural Language Processing

Abstract

In the field of Cross-Domain Knowledge Transfer generative transfer learning in Natural Language Processing (NLP) used for creating text using generative math. The paper aims to improve the usefulness of text creation models in a variety of areas by using cutting edge deep learning and neural network methods. We come up with a new system that makes it easier for information to move from one domain to another, even when the language and situation are different in each domain. Using domain adaptation techniques to match feature distributions and reduce domain gaps is what our method is based on mathematically. It is a complex version of transfer learning principles. We test our model's abilities on a wide range of tasks by doing a lot of careful experiments. We focus on how well it can share information and write text that makes sense and is relevant to the situation across different areas. This study not only adds to our theoretical understanding of cross-domain knowledge transfer, but it also gives us useful tips on how to make NLP models more flexible and useful in the real world. The results of our study could help improve the state of the art in generative transfer learning and make text generation systems that work better and more reliably in a variety of language settings.