Search Results for author: Johannes Daxenberger

Found 20 papers, 11 papers with code

From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems

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.

Relation Classification

Diversity Over Size: On the Effect of Sample and Topic Sizes for Argument Mining Datasets

no code implementations23 May 2022 Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych

The task of Argument Mining, that is extracting argumentative sentences 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 argumentative sentences requires expert knowledge.

Argument Mining Benchmarking +1

How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation

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.

Sentence Embeddings

Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems

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.

Aspect-Controlled Neural Argument Generation

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.

Data Augmentation Language Modelling +1

Stance Detection Benchmark: How Robust Is Your Stance Detection?

1 code implementation6 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.

Fake News Detection Multi-Task Learning +1

Multi-Task Learning for Argumentation Mining in Low-Resource Settings

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.

Multi-Task Learning

Neural End-to-End Learning for Computational Argumentation Mining

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.

Dependency Parsing General Classification +1

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