Maximilian Schmidhuber


2022

pdf bib
MS@IW at SemEval-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated Data
Selina Meyer | Maximilian Schmidhuber | Udo Kruschwitz
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

In this description paper we outline the system architecture submitted to Task 4, Subtask 1 at SemEval-2022. We leverage the generative power of state of the art generative pretrained transformer models to increase training set size and remedy class imbalance issues. Our best submitted system is trained on a synthetically enhanced dataset with 10.3 times as many positive samples as the original dataset and reaches an F1 score of 50.62%, which is 10 percentage points higher than our initial system trained on an undersampled version of the original dataset. We explore possible reasons for the comparably low score in the overall task ranking and report on experiments conducted during the post-evaluation phase.

2021

pdf bib
Universität Regensburg MaxS at GermEval 2021 Task 1: Synthetic Data in Toxic Comment Classification
Maximilian Schmidhuber
Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments

We report on our submission to Task 1 of the GermEval 2021 challenge – toxic comment classification. We investigate different ways of bolstering scarce training data to improve off-the-shelf model performance on a toxic comment classification task. To help address the limitations of a small dataset, we use data synthetically generated by a German GPT-2 model. The use of synthetic data has only recently been taking off as a possible solution to ad- dressing training data sparseness in NLP, and initial results are promising. However, our model did not see measurable improvement through the use of synthetic data. We discuss possible reasons for this finding and explore future works in the field.