  We present an enrichment of the Hateval corpus of hate speech tweets (Basile
et. al 2019) aimed to facilitate automated counter-narrative generation.
Comparably to previous work (Chung et. al. 2019), manually written
counter-narratives are associated to tweets. However, this information alone
seems insufficient to obtain satisfactory language models for counter-narrative
generation. That is why we have also annotated tweets with argumentative
information based on Wagemanns (2016), that we believe can help in building
convincing and effective counter-narratives for hate speech against particular
groups.
  We discuss adequacies and difficulties of this annotation process and present
several baselines for automatic detection of the annotated elements.
Preliminary results show that automatic annotators perform close to human
annotators to detect some aspects of argumentation, while others only reach low
or moderate level of inter-annotator agreement.
