LARGA: Learning Argumentation Axioms from Monological and Dialogical Texts

• Applicants: Professor Dr. Manfred Stede; Professor Dr. Benno Stein
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; General and Comparative Linguistics, Experimental Linguistics, Typology, Non-European Languages
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 455911521

In many real-life situations, from political debates to paper writing, the effectiveness of argumentation depends not only on picking the best arguments, but also on following the best strategy to deliver them. We hence ask: In what order should arguments be presented in a text? What rules or conventions guide these ordering decisions? In what way does a specific linearisation improve or diminish the acceptability of the author's standpoint? Do principles exist that apply across text genres?An example of such organisation knowledge is ``Arguments with units of anecdote type should precede those with units of statistical type.'', which is a pattern often found in editorials. With our research, we want to systematise the identification and the analysis of such patterns (or axioms, as they are called here).Most existing work on computational argumentation concentrates on argument mining and argumentation assessment, while the empirical knowledge about what arrangement strategies are effective for which text genre or mode as well as how to identify such kind of strategy knowledge is missing so far. We want to address this gap by introducing an axiomatic approach for modeling argument arrangement preferences on the basis of ``topic-agnostic'' attributes. We assign these attributes, which we have been compiling in the course of our recent research, to three abstraction levels: the argument unit level, the argument level, and the discourse level. Based on adequate datasets, which have to be properly annotated at these three levels, we seek to uncover and analyse interpretable axiomatic knowledge from argumentative texts of different genres, modes, and levels of writing expertise.Our project provides a concrete plan to acquire the necessary datasets, to induce axioms of the described kind both in monological and dialogical settings, and to analyse interesting relations among these axioms. For instance, how do expert argumentative texts differ from non-expert argumentative texts in terms of these axioms? In addition, we plan to investigate the consequences that our axiom-based approach has on current theories of text structure, and we have devised experiments to demonstrate the benefit of operationalising axiomatic argumentation strategy knowledge in two downstream applications: augmented writing and dialog assistance.The resources developed as part of this project, including the annotations, code, axiomatic knowledge, and prototypical tools, will be made freely available, contributing to the RATIO priority programme and research on argumentation in general.

• DFG Programme: Priority Programmes
• Subproject of: SPP 1999: Robust Argumentation Machines (RATIO)