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Message Passing Attention Networks for Document Understanding

2019-08-17 09:18:47
Giannis Nikolentzos, Antoine J.-P. Tixier, Michalis Vazirgiannis
arXiv_CL
         
Abstract

Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code and data are publicly available.

Abstract (translated)

URL

https://arxiv.org/abs/1908.06267

PDF

https://arxiv.org/pdf/1908.06267.pdf

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