Papers by Andreas Stephan
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Seeing through the mess: evolutionary dynamics of lexical polysemy (2023.emnlp-main)
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| Challenge: | Existing models of lexical polysemy suggest multiple senses emerge for words . lexically, multiple sense of a word is the rule rather than the exception, authors say . |
| Approach: | They propose a mathematical model of the evolution of lexical meaning to investigate polysemy . they find conditions under which a sense of a word diversifies itself into multiple senses . |
| Outcome: | The proposed model predicts that diversification is promoted by low frequency and high discriminability . it also shows that the model is robust to a wide range of language variables . |
Counterfactual Reasoning with Knowledge Graph Embeddings (2024.eacl-long)
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| Challenge: | Knowledge graph embeddings were originally developed to infer true but missing facts in incomplete knowledge repositories. |
| Approach: | They propose a task that requires models to reason on a counterfactual KG. |
| Outcome: | The proposed task connects knowledge graph completion and counterfactual reasoning. |
Stay Focused: Problem Drift in Multi-Agent Debate (2026.findings-eacl)
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| Challenge: | Multi-agent debates have shown promise for solving knowledge and reasoning tasks, but they are limited when solving complex problems that require longer reasoning chains. |
| Approach: | They propose a method to detect problem drift and propose 'driFTJudge' which mitigates 31% of problem drift cases. |
| Outcome: | The proposed method mitigates 31% of problem drift cases and is based on a set of ten tasks across ten different tasks. |
Text-Guided Image Clustering (2024.eacl-long)
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Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth
| Challenge: | Current image clustering methods neglect the use of generated textual descriptions. |
| Approach: | They propose to use image captioning and visual question-answering to cluster images . they propose a new approach to inject task- or domain knowledge into image clustering . |
| Outcome: | The proposed method outperforms existing methods on eight image clustering datasets. |
SepLL: Separating Latent Class Labels from Weak Supervision Noise (2022.findings-emnlp)
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| Challenge: | Existing methods for learning from weak labels use heuristics and heurism to create weak labels. |
| Approach: | They propose a weakly supervised learning paradigm that uses human intuitions to create weak (noisy) labels. |
| Outcome: | The proposed model is competitive with the state-of-the-art and yields a new best average performance. |