3 citations · 6 across the 5 of their papers we have counts for
8 papers · 1 filter
Multilingual Relation Classification via Efficient and Effective Prompting
Yuxuan Chen, David Harbecke, Leonhard Hennig
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual sett…
Why only Micro-F1? Class Weighting of Measures for Relation Classification
David Harbecke, Yuxuan Chen, Leonhard Hennig +1
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro…
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…