@inproceedings{schwarzenberg-etal-2019-layerwise,
title = "Layerwise Relevance Visualization in Convolutional Text Graph Classifiers",
author = {Schwarzenberg, Robert and
H{\"u}bner, Marc and
Harbecke, David and
Alt, Christoph and
Hennig, Leonhard},
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Jansen, Peter and
Glava{\v{s}}, Goran and
Riedl, Martin and
Surdeanu, Mihai and
Vazirgiannis, Michalis",
booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5308",
doi = "10.18653/v1/D19-5308",
pages = "58--62",
abstract = "Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.",
}
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<abstract>Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.</abstract>
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%0 Conference Proceedings
%T Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
%A Schwarzenberg, Robert
%A Hübner, Marc
%A Harbecke, David
%A Alt, Christoph
%A Hennig, Leonhard
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F schwarzenberg-etal-2019-layerwise
%X Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.
%R 10.18653/v1/D19-5308
%U https://aclanthology.org/D19-5308
%U https://doi.org/10.18653/v1/D19-5308
%P 58-62
Markdown (Informal)
[Layerwise Relevance Visualization in Convolutional Text Graph Classifiers](https://aclanthology.org/D19-5308) (Schwarzenberg et al., TextGraphs 2019)
ACL