http://dl.gi.de/handle/20.500.12116/36539 2026-07-21T13:28:33Z http://dl.gi.de/handle/20.500.12116/36540 Frontmatter Frontmatter Article Frontmatter was published on February 1, 2021 in the journal it - Information Technology (volume 63, issue 1). 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36546 A multi-task approach to argument frame classification at variable granularity levels Heinisch, Philipp; Cimiano, Philipp Within the field of argument mining, an important task consists in predicting the frame of an argument, that is, making explicit the aspects of a controversial discussion that the argument emphasizes and which narrative it constructs. Many approaches so far have adopted the framing classification proposed by Boydstun et al. [3], consisting of 15 categories that have been mainly designed to capture frames in media coverage of political articles. In addition to being quite coarse-grained, these categories are limited in terms of their coverage of the breadth of discussion topics that people debate. Other approaches have proposed to rely on issue-specific and subjective (argumentation) frames indicated by users via labels in debating portals. These labels are overly specific and do often not generalize across topics. We present an approach to bridge between coarse-grained and issue-specific inventories for classifying argumentation frames and propose a supervised approach to classifying frames of arguments at a variable level of granularity by clustering issue-specific, user-provided labels into frame clusters and predicting the frame cluster that an argument evokes. We demonstrate how the approach supports the prediction of frames for varying numbers of clusters. We combine the two tasks, frame prediction with respect to media frames categories as well as prediction of clusters of user-provided labels, in a multi-task setting, learning a classifier that performs the two tasks. As main result, we show that this multi-task setting improves the classification on the single tasks, the media frames classification by up to +9.9 % accuracy and the cluster prediction by up to +8 % accuracy. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36544 Argument parsing via corpus queries Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, Lutz We present an approach to extracting arguments from social media, exemplified by a case study on a large corpus of Twitter messages collected under the #Brexit hashtag during the run-up to the referendum in 2016. Our method is based on constructing dedicated corpus queries that capture predefined argumentation patterns following standard Walton-style argumentation schemes. Query matches are transformed directly into logical patterns, i. e. formulae with placeholders in a general form of modal logic. We prioritize precision over recall, exploiting the fact that the sheer size of the corpus still delivers substantial numbers of matches for all patterns, and with the goal of eventually gaining an overview of widely-used arguments and argumentation schemes. We evaluate our approach in terms of recall on a manually annotated gold standard of 1000 randomly selected tweets for three selected high-frequency patterns. We also estimate precision by manual inspection of query matches in the entire corpus. Both evaluations are accompanied by an analysis of inter-annotator agreement between three independent judges. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36543 EVA 2.0: Emotional and rational multimodal argumentation between virtual agents Rach, Niklas; Weber, Klaus; Yang, Yuchi; Ultes, Stefan; André, Elisabeth; Minker, Wolfgang Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface. 2021-01-01T00:00:00Z