Towards a Benchmark of Natural Language Arguments
This work addresses the need for benchmarks in natural language argumentation, which is incremental as it builds on existing frameworks.
The paper tackles the problem of automatically identifying argument relations (attack, support) in natural language by introducing two datasets that combine Textual Entailment with bipolar abstract argumentation, and uses these to compute accepted arguments.
The connections among natural language processing and argumentation theory are becoming stronger in the latest years, with a growing amount of works going in this direction, in different scenarios and applying heterogeneous techniques. In this paper, we present two datasets we built to cope with the combination of the Textual Entailment framework and bipolar abstract argumentation. In our approach, such datasets are used to automatically identify through a Textual Entailment system the relations among the arguments (i.e., attack, support), and then the resulting bipolar argumentation graphs are analyzed to compute the accepted arguments.