Cross-Domain Detection of Abusive Language Online
Mladen Karan, Jan Šnajder · 2018
We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types.To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important.We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over indomain training, especially when used to augment a smaller dataset with a larger one.