ArgumenText

ArgumenText

Decision Support by Means of Automatically Extracting Natural Language Arguments from Big Data

Motivation

In order to make informed decisions, appropriate arguments are needed. However, the mere amount of information and the complexity of many questions frequently prevents us from finding all arguments that are relevant for a reasonable decision. Within the “Decision support by means of automatically extracting natural language arguments from big data” (in short, ArgumenText) project, the UKP Lab develops novel Argument Mining methods for extracting arguments from large and heterogeneous text sources in order to facilitate decision making processes.

In response to a user-defined search query, neural networks determine relevant arguments in realtime and summarize them in a comprehensive way. In contrast to conventional systems, an argumentative information system can show the reasons for or against a decision.

Project webpage: argumentext.de

Goals

The key features of our envisioned argument mining system include:

  • Automatic extraction of arguments from large amounts of heterogeneous texts
  • Detecting pro and con arguments
  • Real-time summarization in natural language
  • Language adaptation of argument mining to German

In addition to supporting decision-making, our argumentative information system will provide an overview of current topics. Its real-time summary of natural language arguments simplifies a wide range of knowledge generation processes in areas such as journalism, politics, and economics.

Methods

In this project, we aim to validate argument mining methods for heterogeneous and large scale texts. Starting from a previously developed joint-modeling method for identifying argument structures in student essays (Stab and Gurevych 2017), we will develop robust end-to-end approaches for mining arguments from web-scale corpora. We also seek to extent our current methods to German using Language Adaptation and evaluate their robustness across various topics and text types. The resulting methods and software are intended to extract arguments from dynamic text sources in real-time and to prepare them for the user in a comprehensive summary.

Team

  • Prof. Dr. Iryna Gurevych, Principal Investigator
  • Dr. Christian Stab, project co-leader
  • Dr. Johannes Daxenberger, project co-leader
  • Dr. Steffen Eger
  • Dr. Tristan Miller
  • Chris Stahlhut
  • Christopher Tauchmann
  • Benjamin Schiller

Funding

The project is funded by the German Federal Ministry of Education and Research (BMBF) as part of the VIP+ program (“Validierung des technologischen und gesellschaftlichen Innovationspotenzials wissenschaftlicher Forschung”).

Publications

Stab, Christian ; Miller, Tristan ; Schiller, Benjamin ; Rai, Pranav ; Gurevych, Iryna (2018):
Cross-topic Argument Mining from Heterogeneous Sources.
In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, In: The 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31.10.2018--04.11.2018, Long Papers, [Online-Edition: http://aclweb.org/anthology/D18-1402],
[Konferenzveröffentlichung]

Eger, Steffen ; Rücklé, Andreas ; Gurevych, Iryna (2018):
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection.
In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, In: 5th Workshop on Argument Mining, Brussels, Belgium, 31.10.2018, [Online-Edition: https://fileserver.ukp.informatik.tu-darmstadt.de/UKP_Webpag...],
[Konferenzveröffentlichung]

Rocha, Gil ; Stab, Christian ; Cardoso, Henrique Lopes ; Gurevych, Iryna (2018):
Cross-Lingual Argumentative Relation Identification: from English to Portuguese.
In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, In: 5th Workshop on Argument Mining, Brussels, Belgium, 31.10.2018, [Online-Edition: http://aclweb.org/anthology/W18-5217],
[Konferenzveröffentlichung]

Stab, Christian ; Daxenberger, Johannes ; Stahlhut, Chris ; Miller, Tristan ; Schiller, Benjamin ; Tauchmann, Christopher ; Eger, Steffen ; Gurevych, Iryna (2018):
ArgumenText: Searching for Arguments in Heterogeneous Sources.
In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: System Demonstrations, In: The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana, 01.06.2018--06.06.2018, [Online-Edition: http://www.aclweb.org/anthology/N18-5005],
[Konferenzveröffentlichung]

Eger, Steffen ; Daxenberger, Johannes ; Stab, Christian ; Gurevych, Iryna (2018):
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
In: Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018), In: The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, USA, 20.08.2018--26.08.2018, [Online-Edition: http://aclweb.org/anthology/C18-1071],
[Konferenzveröffentlichung]

Schulz, Claudia ; Eger, Steffen ; Daxenberger, Johannes ; Kahse, Tobias ; Gurevych, Iryna (2018):
Multi-Task Learning for Argumentation Mining in Low-Resource Settings.
In: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, New Orleans, USA, [Online-Edition: http://aclweb.org/anthology/N18-2006],
[Konferenzveröffentlichung]

Stahlhut, Chris ; Stab, Christian ; Gurevych, Iryna (2018):
Pilot Experiments of Hypothesis Validation Through Evidence Detection for Historians.
In: Proceedings of the First Biennial Conference on Design of Experimental Search & Information Retrieval Systems, In: First Biennial Conference on Design of Experimental Search & Information Retrieval Systems, Bertinoro, Italy, 28.08.2018--31.08.2018, In: CEUR Workshop Proceedings, 2167, [Online-Edition: http://ceur-ws.org/Vol-2167/paper7.pdf],
[Konferenzveröffentlichung]

Daxenberger, Johannes ; Eger, Steffen ; Habernal, Ivan ; Stab, Christian ; Gurevych, Iryna (2017):
What is the Essence of a Claim? Cross-Domain Claim Identification.
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, [Online-Edition: http://aclweb.org/anthology/D17-1218],
[Konferenzveröffentlichung]

Lee, Ji-Ung ; Eger, Steffen ; Daxenberger, Johannes ; Gurevych, Iryna (2017):
UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection.
In: Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, Berlin, Germany, [Online-Edition: https://download.hrz.tu-darmstadt.de/media/FB20/Dekanat/Publ...],
[Konferenzveröffentlichung]

Eger, Steffen ; Daxenberger, Johannes ; Gurevych, Iryna (2017):
Neural End-to-End Learning for Computational Argumentation Mining.
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Association for Computational Linguistics, Vancouver, Canada, Volume 1: Long Papers, [Online-Edition: http://aclweb.org/anthology/P17-1002],
[Konferenzveröffentlichung]

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