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Argumentation is prevalent in scientific discourse and critical to scientific progress. Recent efforts have attempted to identify and model argumentative structures in the scientific literature, but from a diversity of perspectives:
- The scientific literature is a canonical domain in work on computational accounts of argumentation which attempt to model relations between spans and clauses encoding rhetorical structures (e.g., premises and conclusions) or community debate (e.g., supports or attacks).
- Another thread of research, sometimes applied to bioinformatics, focuses on scientific claims and their relation to reported evidence. Much of this work adopts a corpus perspective, aligning claims across documents, using citations to construct claim-evidence networks that summarize the state of knowledge in a field.
- Mainly within the health sciences, argumentative structures are being exploited to automate the production of systematic reviews, by identifying key actionable knowledge elements from collections of clinical reviews, case studies, and the scientific literature.
Despite this abundance of interest and the clear practical importance of the work, we lack consensus on how scientific argumentation should be formalized. It remains unclear whether formalisms popular in non-scientific domains can be adapted for scientific discourse â€“ or even whether a single formalism can adequately support argumentation research in literatures as diverse as biology, chemistry, materials science, and medical science. This manifests in a dearth of shared reference corpora needed to advance research into computational treatments of scientific argumentation, and no consensus on a model for defining argumentative components in scholarly text.
The purpose of this Dagstuhl Seminar is threefold:
- Lay the groundwork for a nascent, multidisciplinary community devoted to building and maintaining principles, tools, and models to identify key components in scholarly argumentation;
- Develop a foundational model for argumentation in science and healthcare, in order to
- Enable robust advances in argument technology for scholarly and medical discourse.
Prior to the seminar, participants will be invited to contribute to the seminar preparation, e.g., by submitting reference material, submitting any corpora and formal descriptions suitable for use in an annotation exercise, or by attending tutorial sessions.
The seminar itself is scheduled to take place over five days, and has the following objectives:
- Knowledge baselining. We will foster a shared understanding of the problem space through a series of keynotes and panel discussions on theory, models, tools, and available corpora.
- Model discussion. Participants will discuss the models introduced during baselining, examining their adequacy, generality, and possibilities for unification under a generalized model.
- Pilot annotation. Breakout groups will apply these models to sample texts, investigating ease of annotation, completeness, and approaches to assessing reliability.
- Application discussion. Building on insights acquired during the seminar, we will discuss the amenability of available models to automated argument mining in various scientific domains and the appropriate success metrics.
- Synthesis. We will draft main conclusions and recommendations to the broader community, identify lingering open questions, and discuss future workshops and shared tasks.
- Khalid Al-Khatib (University of Groningen, NL) [dblp]
- Milad Alshomary (Leibniz Universität Hannover, DE)
- Wolf-Tilo Balke (TU Braunschweig, DE) [dblp]
- Tilman Beck (TU Darmstadt, DE)
- Elena Cabrio (Université Côte d’Azur - Sophia Antipolis, FR) [dblp]
- Fengyu Cai (TU Darmstadt, DE)
- Davide Ceolin (CWI - Amsterdam, NL)
- Anita de Waard (Elsevier - Jericho, US) [dblp]
- Nils Dycke (TU Darmstadt, DE)
- Dayne Freitag (SRI - Menlo Park, US)
- Daniel Garijo (Polytechnic University of Madrid, ES) [dblp]
- Iryna Gurevych (TU Darmstadt, DE) [dblp]
- Graeme Hirst (University of Toronto, CA) [dblp]
- Yufang Hou (IBM Research - Dublin, IE) [dblp]
- Eduard H. Hovy (Carnegie Mellon University, Pittsburgh, US & University of Melbourne, AU) [dblp]
- Anne Lauscher (Universität Hamburg, DE)
- Maria Liakata (Queen Mary University of London, GB)
- Tobias Mayer (TU Darmstadt, DE)
- Robert Mercer (University of Western Ontario - London, CA) [dblp]
- Smaranda Muresan (Columbia University - New York, US) [dblp]
- Preslav Nakov (MBZUAI - Abu Dhabi, AE) [dblp]
- Sukannya Purkayastha (TU Darmstadt, DE)
- Chris Reed (University of Dundee, GB) [dblp]
- Domenic Rosati (scite - Halifax, CA)
- Florian Ruosch (Universität Zürich, CH)
- Harrisen Scells (Universität Leipzig, DE)
- Ferdinand Schlatt (Universität Halle-Wittenberg, DE)
- Benno Stein (Bauhaus-Universität Weimar, DE) [dblp]
- Simone Teufel (University of Cambridge, GB) [dblp]
- Serena Villata (Université Côte d’Azur - Sophia Antipolis, FR) [dblp]
- Andreas Vlachos (University of Cambridge, GB) [dblp]
- Henning Wachsmuth (Universität Paderborn, DE) [dblp]
- Ryan Wang (University of Illinois at Urbana Champaign, US)
- Artificial Intelligence
- Computation and Language
- Machine Learning
- Argument mining
- argument modeling
- scholarly discourse