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.
In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
A. Dargahi Nobari, N. Reshadatmand, and M. Neshati. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, page 2035–2038. New York, NY, USA, Association for Computing Machinery, (2017)
M. Iyyer, A. Guha, S. Chaturvedi, J. Boyd-Graber, and H. Daumé III. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page 1534-1544. The Association for Computational Linguistics, (2016)
J. Lee, F. Dernoncourt, and P. Szolovits. SemEval 2017, (2017)cite arxiv:1704.01523Comment: Accepted at SemEval 2017. The first two authors contributed equally to this work.