Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, Leonhard Hennig
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.- Anthology ID:
- D19-5308
- Volume:
- Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Editors:
- Dmitry Ustalov, Swapna Somasundaran, Peter Jansen, Goran Glavaš, Martin Riedl, Mihai Surdeanu, Michalis Vazirgiannis
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–62
- Language:
- URL:
- https://aclanthology.org/D19-5308
- DOI:
- 10.18653/v1/D19-5308
- Cite (ACL):
- Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, and Leonhard Hennig. 2019. Layerwise Relevance Visualization in Convolutional Text Graph Classifiers. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 58–62, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Layerwise Relevance Visualization in Convolutional Text Graph Classifiers (Schwarzenberg et al., TextGraphs 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D19-5308.pdf
- Code
- DFKI-NLP/lrv