1 citations · 1 across the 19 of their papers we have counts for
6 papers · 2 filters
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models
Patrick Haller, Jonas Golde, Alan Akbik
This paper explores the potential of recurrent neural networks (RNNs) and other subquadratic architectures as competitive alternatives to transformer-based models in low-resource l…
Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data
Jonas Golde, Patrick Haller, Max Ploner +3
Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as 'Person' or 'Medicine') without any training examples. Current research…
TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification Tasks
Lukas Garbas, Max Ploner, Alan Akbik
Classification tasks in NLP are typically addressed by selecting a pre-trained language model (PLM) from a model hub, and fine-tuning it for the task at hand. However, given the ve…
LM-PUB-QUIZ: A Comprehensive Framework for Zero-Shot Evaluation of Relational Knowledge in Language Models
Max Ploner, Jacek Wiland, Sebastian Pohl +1
Knowledge probing evaluates the extent to which a language model (LM) has acquired relational knowledge during its pre-training phase. It provides a cost-effective means of compari…
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Jonas Golde, Felix Hamborg, Alan Akbik
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descr…
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
Max Dallabetta, Conrad Dobberstein, Adrian Breiding +1
This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news s…