3 citations · 4 across the 3 of their papers we have counts for
6 papers
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
David Harbecke
Deep neural networks are powerful statistical learners. However, their predictions do not come with an explanation of their process. To analyze these models, explanation methods ar…
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
David Harbecke, Christoph Alt
Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occl…
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke +2
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 Convolution…
Neural Vector Conceptualization for Word Vector Space Interpretation
Robert Schwarzenberg, Lisa Raithel, David Harbecke
Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method…
Train, Sort, Explain: Learning to Diagnose Translation Models
Robert Schwarzenberg, David Harbecke, Vivien Macketanz +2
Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end lingui…
Learning Explanations from Language Data
David Harbecke, Robert Schwarzenberg, Christoph Alt
PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks. We demonstrate that it also generates meaningful inte…