Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers

1 code implementation • Findings (ACL) 2022 • Jakob Smedegaard Andersen, Walid Maalej

To maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers' output.

Benchmarking

REM: Efficient Semi-Automated Real-Time Moderation of Online Forums

no code implementations • ACL 2021 • Jakob Smedegaard Andersen, Olaf Zukunft, Walid Maalej

This paper presents REM, a novel tool for the semi-automated real-time moderation of large scale online forums.

Forum 4.0: An Open-Source User Comment Analysis Framework

no code implementations • EACL 2021 • Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian St{\"o}cker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej

With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging.

BIG-bench Machine Learning

Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs

1 code implementation • COLING 2020 • Jakob Smedegaard Andersen, Tom Sch{\"o}ner, Walid Maalej

Estimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications.

Decision Making Sentiment Analysis