85 citations · 190 across the 7 of their papers we have counts for
7 papers
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Andreas Hanselowski, Christian Stab, Claudia Schulz +2
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machin…
Classification and Clustering of Arguments with Contextualized Word Embeddings
Nils Reimers, Benjamin Schiller, Tilman Beck +3
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the p…
Fine-Grained Argument Unit Recognition and Classification
Dietrich Trautmann, Johannes Daxenberger, Christian Stab +2
Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both fr…
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Steffen Eger, Johannes Daxenberger, Christian Stab +1
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existin…
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
Christian Stab, Tristan Miller, Iryna Gurevych
Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches to argument mining…
What is the Essence of a Claim? Cross-Domain Claim Identification
Johannes Daxenberger, Steffen Eger, Ivan Habernal +2
Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argumen…
Parsing Argumentation Structures in Persuasive Essays
Christian Stab, Iryna Gurevych
In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint…