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

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]

Recognizing Insufficiently Supported Arguments in Argumentative Essays

Christian Stab, Iryna Gurevych
In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), p. to appear, April 2017
Association for Computational Linguistics
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/insufficiently-supported-arguments]
[Inproceedings]

Domain-Specific Aspects of Scientific Reasoning and Argumentation: Insights from Automatic Coding

Johannes Daxenberger, Andras Csanadi, Christian Ghanem, Ingo Kollar, Iryna Gurevych
In: Scientific Reasoning and Argumentation: Domain-Specific and Domain-General Aspects, p. to appear, 2017
Taylor & Francis
[InCollection]

Argumentation Mining in User-Generated Web Discourse

Ivan Habernal, Iryna Gurevych
In: Computational Linguistics, Vol. 43, p. (in press), 2017
[Online-Edition: http://arxiv.org/abs/1601.02403]
[Article]

What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation

Ivan Habernal, Iryna Gurevych
In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 1214-1223, November 2016
Association for Computational Linguistics
[Online-Edition: https://github.com/UKPLab/emnlp2016-empirical-convincingness]
[Inproceedings]

3rd Workshop on Argument Mining

Chris Reed, Kevin Ashley, Claire Cardie, Nancy Green, Iryna Gurevych, Diane Litman, Georgios Petasis, Noam Slonim, Vern Walker
August 2016
Association for Computational Linguistics
[Online-Edition: http://argmining2016.arg.tech/]
[Proceedings]

Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM

Ivan Habernal, Iryna Gurevych
In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p. 1589-1599, August 2016
Association for Computational Linguistics
[Online-Edition: https://github.com/UKPLab/acl2016-convincing-arguments]
[Inproceedings]

Recognizing the Absence of Opposing Arguments in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016), p. 113-118, August 2016
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/opposing-arguments-in-persuasive-essays]
[Inproceedings]

Argumentation: Content, Structure, and Relationship with Essay Quality

Beata Beigman Klebanov, Christian Stab, Jill Burstein, Yi Song, Binod Gyawali, Iryna Gurevych
In: Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016), p. 70-75, August 2016
[Inproceedings]
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