Papers by Walid Maalej
Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers (2022.findings-acl)
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| Challenge: | A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) |
| Approach: | They propose a semi-automated approach that passes unconfident, probably incorrect classifications to human moderators to minimize the workload. |
| Outcome: | The proposed approach can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) while reducing the moderation load up to 73.3% compared to a random moderation. |
Forum 4.0: An Open-Source User Comment Analysis Framework (2021.eacl-demos)
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Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian Stöcker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
| Challenge: | Using Forum 4.0, we analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Approach: | They introduce an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Outcome: | The proposed framework can analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs (2020.coling-main)
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| Challenge: | Neural Networks are not interpretable, since they provide no information about why particular decisions were made. |
| Approach: | They propose to decompose and visualize uncertainty of text classifiers at the level of words to provide detailed explanations of uncertainties. |
| Outcome: | The proposed approach decomposes and visualizes uncertainty of text classifiers at the level of words and enables a deeper understanding of unreliable model behaviours. |
REM: Efficient Semi-Automated Real-Time Moderation of Online Forums (2021.acl-demo)
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| Challenge: | REM is a tool for the semi-automated real-time moderation of large scale online forums. |
| Approach: | They propose a semi-automated real-time moderation tool for large scale online forums that maximizes the efficiency of manual moderation by targeting only those comments for which human intervention is needed. |
| Outcome: | The proposed method maximizes the efficiency of manual moderation by targeting only those comments for which human intervention is needed, e.g. due to high classification uncertainty. |