Taking as a case study the Hate Speech Detection task at EVALITA 2018, the paper discusses the distribution and typology of the errors made by the five best-scoring systems. The focus is on the sub-task where Twitter data was used both for training and testing (HaSpeeDe-TW). In order to highlight the complexity of hate speech and the reasons beyond the failures in its automatic detection, the annotation provided for the task is enriched with orthogonal categories annotated in the original reference corpus, such as aggressiveness, offensiveness, irony and the presence of stereotypes.
Error analysis in a hate speech detection task: The case of Haspeede-TW at Evalita 2018
Sanguinetti Manuela
2019-01-01
Abstract
Taking as a case study the Hate Speech Detection task at EVALITA 2018, the paper discusses the distribution and typology of the errors made by the five best-scoring systems. The focus is on the sub-task where Twitter data was used both for training and testing (HaSpeeDe-TW). In order to highlight the complexity of hate speech and the reasons beyond the failures in its automatic detection, the annotation provided for the task is enriched with orthogonal categories annotated in the original reference corpus, such as aggressiveness, offensiveness, irony and the presence of stereotypes.
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