HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
Anna Koufakou, Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti · 2020
The detection of abusive or offensive remarks in social texts has received significant attention in research.In several related shared tasks, BERT has been shown to be the state-of-theart.In this paper, we propose to utilize lexical features derived from a hate lexicon towards improving the performance of BERT in such tasks.We explore different ways to utilize the lexical features in the form of lexicon-based encodings at the sentence level or embeddings at the word level.We provide an extensive dataset evaluation that addresses in-domain as well as cross-domain detection of abusive content to render a complete picture.Our results indicate that our proposed models combining BERT with lexical features help improve over a baseline BERT model in many of our indomain and cross-domain experiments.