Studying Generalisability across Abusive Language Detection Datasets

Steve Durairaj Swamy, Anupam Jamatia, Björn Gambäck


Abstract
Work on Abusive Language Detection has tackled a wide range of subtasks and domains. As a result of this, there exists a great deal of redundancy and non-generalisability between datasets. Through experiments on cross-dataset training and testing, the paper reveals that the preconceived notion of including more non-abusive samples in a dataset (to emulate reality) may have a detrimental effect on the generalisability of a model trained on that data. Hence a hierarchical annotation model is utilised here to reveal redundancies in existing datasets and to help reduce redundancy in future efforts.
Anthology ID:
K19-1088
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
940–950
Language:
URL:
https://www.aclweb.org/anthology/K19-1088
DOI:
10.18653/v1/K19-1088
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1088.pdf