1 code implementation • SIGDIAL (ACL) 2021 • Niklas Rach, Carolin Schindler, Isabel Feustel, Johannes Daxenberger, Wolfgang Minker, Stefan Ultes
Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations.
no code implementations • 6 Mar 2023 • Nina Mouhammad, Johannes Daxenberger, Benjamin Schiller, Ivan Habernal
Most tasks in NLP require labeled data.
1 code implementation • 23 May 2022 • Benjamin Schiller, Johannes Daxenberger, Andreas Waldis, Iryna Gurevych
The task of Argument Mining, that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argument components requires expert knowledge.
1 code implementation • NAACL 2021 • Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych
Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.
1 code implementation • CONLL 2020 • Steffen Eger, Johannes Daxenberger, Iryna Gurevych
We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other languages.
no code implementations • LREC 2020 • Niklas Rach, Yuki Matsuda, Johannes Daxenberger, Stefan Ultes, Keiichi Yasumoto, Wolfgang Minker
We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments.
1 code implementation • NAACL 2021 • Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych
In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect.
1 code implementation • 6 Jan 2020 • Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
2 code implementations • ACL 2019 • Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, Iryna Gurevych
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
1 code implementation • 22 Apr 2019 • Dietrich Trautmann, Johannes Daxenberger, Christian Stab, Hinrich Schütze, Iryna Gurevych
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
1 code implementation • COLING 2018 • Steffen Eger, Johannes Daxenberger, Christian Stab, Iryna Gurevych
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually.
no code implementations • NAACL 2018 • Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
Argument mining is a core technology for enabling argument search in large corpora.
1 code implementation • NAACL 2018 • Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, Iryna Gurevych
We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification.
no code implementations • WS 2017 • Maria Sukhareva, Francesco Fuscagni, Johannes Daxenberger, Susanne G{\"o}rke, Doris Prechel, Iryna Gurevych
To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.
1 code implementation • EMNLP 2017 • Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
Argument mining has become a popular research area in NLP.
2 code implementations • ACL 2017 • Steffen Eger, Johannes Daxenberger, Iryna Gurevych
Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.