Institute for Web Science and Technologies · Universität Koblenz
Institute WeST

DFG Project: Open Argument Mining

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Open debates include so many arguments that sound decision making exceeds cognitive capabilities of the interested public or responsible experts.

Open debates include so many arguments that sound decision making exceeds cognitive capabilities of the interested public or responsible experts. New arguments are continuously contributed (challenge C1), are often incomplete (C2), and knowledge about common facts or previous arguments is needed to understand them (C3). This project aims at investigating computational methods that i) continuously improve their capability to recognize arguments in ongoing debates, ii) align incomplete arguments with previous arguments and enrich them with automatically acquired background knowledge, and iii) constantly extend semantic knowledge bases with information required to understand arguments.

We achieve this by combining and advancing current state-of-the-art algorithms from the two research fields argument mining and knowledge graph construction. To deal with concept drifts in ongoing debates, we aim to advance argument mining methods with a knowledge-aware lifelong learning approach. We will investigate novel neural architectures for learning topic invariant argument features and the relation between arguments and debate topics, inject semantic knowledge into the neural network using knowledge graph embeddings and leverage self-training to continuously extend the training data. To cope with incomplete arguments, the retrieved arguments will be aligned with known arguments and enriched with background knowledge. We will link the entities of arguments to background knowledge by combining link discovery and keyword search. This linked background knowledge will be incorporated into incremental clustering methods for grouping similar arguments into argument clusters. Argumentative support and attack relations between these argument clusters will be determined using supervised learning. We aim to automatically acquire the required background knowledge by combining contemporary semantic knowledge bases containing encyclopedic and commonsense knowledge (Babelnet and ConceptNet) and focused knowledge extraction from unstructured Web corpora (Common Crawl). To integrate this background knowledge into machine learning models, we are going to adopt existing knowledge embedding techniques to support incremental training. Furthermore, this project focuses on developing novel annotation schemes and new benchmark corpora allowing us to evaluate our mining and alignment methods across topics, text types, and varying timestamps.

The outcome will be novel methods for obtaining an Open Argumentation Graph including semantically enriched groups of similar arguments from multiple textual sources linked with support and attack relations. To ensure a wide coverage of argumentation styles, we will apply our methods to different topics frequently discussed in online news and Twitter messages and conduct both component evaluation using annotated gold-data and crowd-based post-hoc evaluations.


  • Operating Time: 02/2019 - 01/2022
  • Source of Funding: DFG – Deutsche Forschungsgemeinschaft
  • Partner:
    • Iryna Gurevych, TU Darmstadt
    • Christian Stab, TU Darmstadt


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