Mining, Assessing, and Improving Arguments in NLP and the Social Sciences
Tutorial @ EACL, Dubrovnik, May 5th 2023, 9:00 - 18.00
Computational argumentation is an interdisciplinary research field, connecting NLP to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention, argument quality assessment: what makes an argument good or bad?
The tutorial will be structured along three coordinates:
the notions of argument quality across disciplines: how do we recognize good and bad arguments?
the modeling of subjectivity: who argues to whom; what are their beliefs?
the generation of improved arguments: what makes an argument better?
A key feature of this tutorial is its interactive nature: We will involve the participants in two annotation studies on the assessment and the improvement of quality, and we will encourage them to reflect on the challenges and potential of these tasks.
Instructors
Gabriella Lapesa, University of Stuttgart
Eva Maria Vecchi, University of Stuttgart
Serena Villata, Laboratoire I3S, CNRS, Sophia Antipolis, France
Henning Wachsmuth, Leibniz University Hannover
Session 1 - Mining arguments [slides]
Session 2 - The NLP perspective: Argument Quality [slides]
Session 3 - Interactive session: Argument Quality [slides]
Session 4 - The Social Science perspective: Deliberative Quality [slides]
Session 5 - Modeling Subjectivity [slides]
Session 6 - Interactive session: Improving arguments [slides]
Session 7 - Generation methods for improving arguments [slides]
Session 8 - Wrapping up [slides]
Recommended readings
Five Years of Argument Mining: a Data-driven Analysis (Cabrio and Villata, 2018)
Towards Argument Mining for Social Good: a Survey (Vecchi et al. 2021)
Computational Argumentation Quality Assessment in Natural Language (Wachsmuth et al. 2017)
Support to the interactive sessions
Gabriella Skitalinska, Leibniz University Hannover