Argumentative Writing Support

Motivation

Formulating persuasive and well-formed arguments is a challenging task and a crucial aspect in writing skills acquisition. However, current writing support is limited to feedback about grammar or spelling and there is no system that provides formative feedback about argumentative writing. In this project, we aim to research novel methods for assisting authors in writing persuasive arguments with respect to the following questions:

  • Is my argument well structured and comprehensible?
  • Are the given reasons relevant for my claim?
  • Does my argument include sufficient support for being persuasive?

Goals

  • Create tools which aid in improving argumentation quality
  • Develop methods for identifying argumentation structures in text
  • Investigate novel models which automatically assess argumentation quality
  • Provide formative feedback about argumentation

Methods

The research methods of Argumentative Writing Support (AWS) include three consecutive steps/tasks:

1. Identification of argumentation structure: Separation of argumentative and non-argumentative text units and recognition of argument components by means of state-of-the-art Natural Language Processing (NLP) techniques.

2. Assessment of argumentation quality: Development of novel techniques for identifying flaws in the argumentation structure, assessing the type of reasoning, and evaluating appropriateness of the given support.

3. Formative feedback: Integration of the methods in writing environments and visualization of quality flaws in the text document in order to support authors in revising their arguments.

Data

Partners

  • Holtzbrinck Publishing Group
  • Macmillan Science & Education

People

Project Publications

Additional Attributes

Type

Fallsimulationen und automatisches adaptives Feedback mittels Künstlicher Intelligenz in digitalen Lernumgebungen

Claudia Schulz, Michael Sailer, Jan Kiesewetter, Christian M. Meyer, Iryna Gurevych, Frank Fischer, Martin R. Fischer
In: e-teaching.org Themenspecial „Was macht Lernen mit digitalen Medien erfolgreich?“, p. 1-14, October 2017
[Online-Edition: https://www.e-teaching.org/praxis/erfahrungsberichte/fallsimulationen-und-automatisches-adaptives-feedback-mittels-kuenstlicher-intelligenz-in-digitalen-lernumgebungen]
[Article]

Argotario: Computational Argumentation Meets Serious Games

Ivan Habernal, Raffael Hannemann, Christian Pollak, Christopher Klamm, Patrick Pauli, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, p. 7-12, September 2017
Association for Computational Linguistics
[Online-Edition: https://github.com/UKPLab/argotario]
[Inproceedings]

Argumentation Mining: Eine neue Methode zur automatisierten Textanalyse und ihre Anwendung in der Kommunikationswissenschaft

Markus Maurer, Johannes Daxenberger, Iryna Gurevych
In: Jahrestagung der Fachgruppe Methoden der Publizistik- und Kommunikationswissenschaft der Deutschen Gesellschaft für Publizistik- und Kommunikationswissenschaft, September 2017
[Inproceedings]

What is the Essence of a Claim? Cross-Domain Claim Identification

Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 2055–2066, September 2017
[Inproceedings]

Parsing Argumentation Structures in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Computational Linguistics, Vol. 43, p. 619--659, September 2017
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-essays-version-2]
[Article]

UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection

Ji-Ung Lee, Steffen Eger, Johannes Daxenberger, Iryna Gurevych
In: Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, p. 22-29, September 2017
[Inproceedings]

Training Argumentation Skills with Argumentative Writing Support

Christian Stab, Iryna Gurevych
In: Proceedings of the 21st Workshop on the Semantics and Pragmatics of Dialogue, p. 174--175, August 2017
[Online-Edition: http://www.saardial.uni-saarland.de/wordpress/wp-content/uploads/SemDial2017SaarDial_proceedings.pdf]
[Inproceedings]

The Argument Reasoning Comprehension Task

Ivan Habernal, Iryna Gurevych, Henning Wachsmuth, Benno Stein
August 2017
[Online-Edition: https://github.com/UKPLab/argument-reasoning-comprehension-task]
[Techreport]

Argumentation Quality Assessment: Theory vs. Practice

Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, Benno Stein
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 2: Short Papers, p. 250-255, August 2017
Association for Computational Linguistics
[Inproceedings]

Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (ACM TOIT)

Iryna Gurevych, Marco Lippi, Paolo Torroni
Vol. 17, August 2017
Association for Computing Machinery
[Book]
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