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Exploiting Background Knowledge for Argumentative Relation Classification

Authors Jonathan Kobbe, Juri Opitz, Maria Becker, Ioana Hulpuş, Heiner Stuckenschmidt, Anette Frank

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Author Details

Jonathan Kobbe
  • University of Mannheim, Germany
Juri Opitz
  • Heidelberg University, Germany
Maria Becker
  • Heidelberg University, Germany
Ioana Hulpuş
  • University of Mannheim, Germany
Heiner Stuckenschmidt
  • University of Mannheim, Germany
Anette Frank
  • Heidelberg University, Germany

Funding

This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project ExpLAIN, Grant Number STU 266/14-1 and FR 1707/-4-1, as part of the Priority Program "Robust Argumentation Machines (RATIO)" (SPP-1999) as well as by the Leibniz Science Campus Empirical Linguistics & Computational Language Modeling, supported by Leibniz Association grant no. SAS2015-IDS-LWC and by the Ministry of Science, Research, and Art of Baden-Württemberg.

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation through a donation of GPUs that were used for this research.

Cite AsGet BibTex

Jonathan Kobbe, Juri Opitz, Maria Becker, Ioana Hulpuş, Heiner Stuckenschmidt, and Anette Frank. Exploiting Background Knowledge for Argumentative Relation Classification. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 8:1-8:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.8

Abstract

Argumentative relation classification is the task of determining the type of relation (e.g., support or attack) that holds between two argument units. Current state-of-the-art models primarily exploit surface-linguistic features including discourse markers, modals or adverbials to classify argumentative relations. However, a system that performs argument analysis using mainly rhetorical features can be easily fooled by the stylistic presentation of the argument as opposed to its content, in cases where a weak argument is concealed by strong rhetorical means. This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based understanding and representation of arguments. We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. We evaluate our systems on three different datasets and show that the inclusion of background knowledge can improve the classification performance by considerable margins. Thus, our work offers a first step towards effective, knowledge-rich argument analysis.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Neural networks
  • Information systems → Graph-based database models
  • Information systems → Clustering and classification
Keywords
  • argument structure analysis
  • background knowledge
  • argumentative functions
  • argument classification
  • commonsense knowledge relations

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