Check out my class talk slides about Graph Neural Networks and their applications in NLP!
Covered materials:
- Semi-Supervised Classification with Graph Convolutional Networks
- Variational Graph Auto-Encoders
- Graph Attention Networks
- Graph Convolutional Networks for Text Classification (AAAI 2019)
- Heterogeneous Graph Neural Networks for Extractive Document Summarization (ACL 2020)
- A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction (ACL 2020)
My related works:
- R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Swapnil Hingmire, and Dragomir Radev
COLING, 2020. Github
- What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Robert Tung, and Dragomir Radev
Proceedings of AAAI 2019, Dataset BLOG
