Talk: Bridging the gap: exploring the applications of graph neural networks in NLP

Title

Bridging the gap: exploring the applications of graph neural networks in NLP

Summary

In this talk, Shuzhou will introduce the applications of graph neural networks in NLP, covering text generation, text classification, and multilingual learning. He will also discuss future directions in utilizing graph neural networks for enhancing the controllability and transparency of large language models.

Bio

Shuzhou Yuan is a second-year PhD student at Karlsruhe Institute of Technology, soon transitioning to Dresden University of Technology under Prof. Michael Färber’s supervision. Previously, he pursued his master’s studies at University of Munich, focusing on adversarial training for hate speech classification in Hinrich Schütze’s group. Currently, Shuzhou is dedicated to exploring the applications of graph neural networks for NLP, with a focus on developing low-resource methods for large language models.