Natural Language Processing

Transformers are Graph Neural Networks

Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.

An Experimental Comparison of Text Classification Techniques

Text classification is the task of labeling text data from a predetermined set of thematic labels. It has become of increasing importance in recent years as we generate large volumes of data and require the ability to search through these vast …