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Dataset Independent Baselines for Relation Prediction in Argument Mining

Oana Cocarascu 1, 2 Elena Cabrio 3 Serena Villata 3 Francesca Toni 1
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Argument(ation) Mining (AM) is the research area which aims at extracting argument components and predicting argumentative relations (i.e., support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources were created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in AM. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task in AM. Thus, our base-lines can be employed by the AM community to compare more effectively how well a method performs on the argumentative relation prediction task.
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Submitted on : Tuesday, October 20, 2020 - 11:34:45 AM
Last modification on : Thursday, January 21, 2021 - 2:32:02 PM
Long-term archiving on: : Thursday, January 21, 2021 - 6:51:47 PM

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Oana Cocarascu, Elena Cabrio, Serena Villata, Francesca Toni. Dataset Independent Baselines for Relation Prediction in Argument Mining. COMMA 2020 - 8th International Conference on Computational Models of Argument, Sep 2020, Perugia, Italy. pp.45-52, ⟨10.3233/FAIA200490⟩. ⟨hal-02972180⟩

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