Fetching the paper…
Reading the bibliography…
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa · 2011
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Earlier work this paper cites.
Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. Müller · 2016
Earlier work this paper cites.
Visualizing and understanding neural models in nlp
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky · 2016
Cited alongside, same era.
Explaining predictions of non-linear classifiers in nlp
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2016
Cited alongside, same era.
Very deep convolutional networks for text classification
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun · 2017
Cited alongside, same era.
" what is relevant in a text document?": An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2017
Later among the works it cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Oztireli, and Markus Gross · 2018
Closest in time.
Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…