Overview

Argumentation is omnipresent in our daily communication and an important part of each decision making process. The recent research field of Argumentation Mining aims at automatically recognizing argumentation structures in written discourse in order to establish new intelligent systems for facilitating information access, writing skills acquisition and text summarization. This research area includes the following objectives:

  • Identifying argument components in different text types

  • Recognizing relations between argument components

  • Automatic assessment of argumentation quality

The research at UKP focuses on analyzing argumentation structures in written discourse. Our recent work is concerned with the analysis of argumentation structures in user-generated Web content (Habernal & Gurevych, 2015), scientific articles (Kirschner et al., 2015), and student texts (Stab & Gurevych, 2014).

Current Projects

  • Argumentative Writing Support (AWS): The goal of this project is to develop a novel writing assistance system in order to support authors in writing persuasive arguments and to improve their writing skills.

  • Large-scale argumentation mining on the Web: We aim at analyzing argumentation in various types of user-generated Web content, such as comments to articles, discussion forums, or blogs with the goal to overcome the current information overload and support users in decision-making.

  • Knowledge extraction and consolidation: This project focuses on the analysis of argumentation structures in scientific publications on a fine-grained level. The goal is to reveal how an author connects her thoughts in order to create a convincing line of argumentation. Such a fine-grained analysis of the argumentation structure will enable new ways information access, and could be integrated, for example, in summarization or faceted search applications as part of digital libraries.

  • ArguAna: Argumentation mining deals with the automatic identification of arguments and their relations from natural language text. This research project targets at the specific challenges of argumentation mining for the web. We seek to establish foundations of algorithms that apply argument mining to various forms of web argumentation, efficiently leverage the scale of the web, and complement argumentation mining with an argumentation analysis to effectively assess important quality dimensions.

Events

Resources

  • Argument Annotated Essays: A corpus of persuasive essays annotated with argumentation structures.
  • Argument Annotated Essays (version 2): An extended corpus of persuasive essays annotated with argumentation structures.
  • Argument Annotated User-Generated Web Discourse: A corpus contains user comments, forum posts, blogs and newspaper articles annotated with argument scheme based on extended Toulmin's model
  • Argument Annotated News Articles: A corpus of German documents on controversial educational topics (crawled from the Web, ca. 80% news articles) annotated with arguments according to the claim-premises scheme.
  • Argument Annotated Scientific Articles: A corpus of German scientific articles from the field of educational research, annotated with graph-structures of argumentative relations.
  • UKPConvArg1 Corpus: A corpus of 16k pairs of arguments for studying convincingness of Web arguments, as presented in our ACL 2016 paper.
  • UKPConvArg2 Corpus: A crowd-sourced corpus containing 9,111 argument pairs, multi-labeled with 17 classes, which was cleaned and curated by employing several strict quality measures. We proposed two tasks on this data set in our EMNLP 2016 paper, namely predicting the full label distribution and  classifying types of flaws in less convincing arguments.
  • Opposing Arguments: A corpus of 402 persuasive essays annotated with myside biases.
  • Insufficiently Supported Arguments: A corpus of 1,029 arguments annotated with the sufficiency criterion.

Software

Reference publications

Additional Attributes

Type

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. 125-179, 2017
[Online-Edition: http://dx.doi.org/10.1162/COLI_a_00276]
[Article]

Parsing Argumentation Structures in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Computational Linguistics, p. (in press), 2017
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-essays-version-2]
[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]

Mass Collaboration on the Web: Textual Content Analysis by Means of Natural Language Processing

Ivan Habernal, Johannes Daxenberger, Iryna Gurevych
In: Mass Collaboration and Education, Vol. 16, p. 367-390, February 2016
Springer International Publishing
[Online-Edition: http://doi.org/10.1007/978-3-319-13536-6_18]
[InCollection]

Detecting Argument Components and Structures

Christian Stab, Ivan Habernal
In: Report of Dagstuhl Seminar on Debating Technologies (15512), Vol. 5, p. 32-32, 2016
[Online-Edition: http://www.dagstuhl.de/15512]
[Article]

Cooperation partners

  • Educational Testing Service (NLP division)

  • Prof. Fischer (network of excellence)

  • Prof. Dr. Benno Stein

  • Macmillan Science and Education

Primary Contact

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