@inbook{bda4984a164d4b6486bfedffd8c2ebe3,
title = "Dataset independent baselines for relation prediction in argument mining",
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 baselines can be employed by the AM community to compare more effectively how well a method performs on the argumentative relation prediction task.",
keywords = "Argument Mining, Machine Learning Methods, Relation Prediction",
author = "Oana Cocarascu and Elena Cabrio and Serena Villata and Francesca Toni",
year = "2020",
month = aug,
day = "31",
doi = "10.3233/FAIA200490",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "45--52",
editor = "Henry Prakken and Stefano Bistarelli and Francesco Santini and Carlo Taticchi",
booktitle = "Computational Models of Argument - Proceedings of COMMA 2020",
note = "8th International Conference on Computational Models of Argument, COMMA 2020 ; Conference date: 08-09-2020 Through 11-09-2020",
}