6 citations · 9 across the 3 of their papers we have counts for
10 papers
Towards a Holistic View on Argument Quality Prediction
Michael Fromm, Max Berrendorf, Johanna Reiml +4
Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing at…
Active Learning for Argument Strength Estimation
Nataliia Kees, Michael Fromm, Evgeniy Faerman +1
High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument…
Adaptive Multi-Resolution Attention with Linear Complexity
Yao Zhang, Yunpu Ma, Thomas Seidl +1
Transformers have improved the state-of-the-art across numerous tasks in sequence modeling. Besides the quadratic computational and memory complexity w.r.t the sequence length, the…
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification
Julian Busch, Anton Kocheturov, Volker Tresp +1
Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially. While some…
Argument Mining Driven Analysis of Peer-Reviews
Michael Fromm, Evgeniy Faerman, Max Berrendorf +7
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process a…
Diversity Aware Relevance Learning for Argument Search
Michael Fromm, Max Berrendorf, Sandra Obermeier +2
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims…