43 citations · 52 across the 8 of their papers we have counts for
12 papers
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Nils Feldhus, Leonhard Hennig, Maximilian Dustin Nasert +3
Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many fe…
Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools
Nils Feldhus, Robert Schwarzenberg, Sebastian Möller
In the language domain, as in other domains, neural explainability takes an ever more important role, with feature attribution methods on the forefront. Many such methods require c…
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Marc Hübner, Christoph Alt, Robert Schwarzenberg +1
Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Ta…
Efficient Explanations from Empirical Explainers
Robert Schwarzenberg, Nils Feldhus, Sebastian Möller
Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate computationally expensive explainers. To this end,…
Pattern-Guided Integrated Gradients
Robert Schwarzenberg, Steffen Castle
Integrated Gradients (IG) and PatternAttribution (PA) are two established explainability methods for neural networks. Both methods are theoretically well-founded. However, they wer…
Evaluating German Transformer Language Models with Syntactic Agreement Tests
Karolina Zaczynska, Nils Feldhus, Robert Schwarzenberg +2
Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most state-of-the-art NLP models now operate on top of TLMs to benefit f…