54 citations · 84 across the 18 of their papers we have counts for
6 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…
Full-Text Argumentation Mining on Scientific Publications
Arne Binder, Bhuvanesh Verma, Leonhard Hennig
Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two…
Confidence estimation of classification based on the distribution of the neural network output layer
Abdel Aziz Taha, Leonhard Hennig, Petr Knoth
One of the most common problems preventing the application of prediction models in the real world is lack of generalization: The accuracy of models, measured in the benchmark does…
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…
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…
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Yuxuan Chen, Jonas Mikkelsen, Arne Binder +2
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-…