5 citations · 7 across the 3 of their papers we have counts for
4 papers
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers
Marius Mosbach, Anna Khokhlova, Michael A. Hedderich +1
Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguisti…
Learning Functions to Study the Benefit of Multitask Learning
Gabriele Bettgenhäuser, Michael A. Hedderich, Dietrich Klakow
We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly.…
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
David Ifeoluwa Adelani, Michael A. Hedderich, Dawei Zhu +2
The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countr…
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
Lukas Lange, Michael A. Hedderich, Dietrich Klakow
In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but ofte…