123 citations · 139 across the 4 of their papers we have counts for
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Understanding Text Classification Data and Models Using Aggregated Input Salience
Sebastian Ebert, Alice Shoshana Jakobovits, Katja Filippova
Realizing when a model is right for a wrong reason is not trivial and requires a significant effort by model developers. In some cases an input salience method, which highlights th…
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia +2
Feature attribution a.k.a. input salience methods which assign an importance score to a feature are abundant but may produce surprisingly different results for the same model on th…
Attention-Based Convolutional Neural Network for Machine Comprehension
Wenpeng Yin, Sebastian Ebert, Hinrich Schütze
Understanding open-domain text is one of the primary challenges in natural language processing (NLP). Machine comprehension benchmarks evaluate the system's ability to understand t…
Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe, Sebastian Ebert, Hinrich Schütze
Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding…