Cross-Domain Detection of Abusive Language Online (CROSBI ID 681149)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Karan, Mladen ; Šnajder, Jan
engleski
Cross-Domain Detection of Abusive Language Online
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.
domain adaptation, abusive language
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Podaci o prilogu
132-137.
2018.
objavljeno
10.18653/v1/w18-5117
Podaci o matičnoj publikaciji
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
Association for Computational Linguistics (ACL)
Podaci o skupu
Association for Computational Linguistics, Second Workshop on Abusive Language Online (ALW2)
poster
31.10.2018-04.11.2018
Bruxelles, Belgija