Argumentation Mining on the Web from Information Seeking Perspective

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Probing Neural Network Comprehension of Natural Language Arguments (2019, background): “This is a challenging task for machine learners, as it can be hard even for humans to determine when two text segments stand in argumentative relation, as evidenced by studies on argument annotation (Habernal et al., 2014).”
DART: a Dataset of Arguments and their Relations on Twitter (2016, unknown): “…annotated corpora are needed, like for instance those proposed in (Reed and Rowe, 2004; Palau and Moens, 2011; Levy et al., 2014; Aharoni et al., 2014; Stab and Gurevych, 2014; Cabrio and Villata, 2014; Habernal et al., 2014), to be used as a training set for any kind of aforementioned prediction.”
Cross-topic Argument Mining from Heterogeneous Sources (2018, background): “Indeed, as Habernal et al. (2014) observe, there is a great diversity of perspectives on how arguments can be best characterized and modelled, and no “one-size-ﬁts-all” argumentation theory that applies to the variety of text sources found on the Web.”