Summary of Argumentation and Machine Learning, by Antonio Rago et al.
Argumentation and Machine Learning
by Antonio Rago, Kristijonas Čyras, Jack Mumford, Oana Cocarascu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This chapter presents an overview of research approaches that combine Computational Argumentation and Machine Learning. The authors identified two themes: argumentation for machine learning and machine learning for argumentation. They systematically evaluated works across various dimensions, including the type of learning and argumentation framework used. Three types of interaction were identified: synergistic, segmented, and approximated approaches. The chapter concludes with insights on the suitability of certain forms of Argumentation for supporting Machine Learning and vice versa, highlighting patterns and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how two fields, Computational Argumentation and Machine Learning, can work together. It looks at different ways these areas interact and what each does well or poorly. The authors found that some approaches are closely connected, while others use one method as input for the other. They also saw patterns on which type of argumentation works best with certain machine learning techniques. Overall, this chapter shows how combining these two fields can be beneficial but also highlights challenges to overcome. |
Keywords
» Artificial intelligence » Machine learning
