Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection


The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario. As human annotation involves substantial time and effort, models that can adapt to newly collected comments can prove to be useful. In this paper, we investigate the effectiveness of several Unsupervised Domain Adaptation (UDA) approaches for the task of cross-corpora abusive language detection. In comparison, we adapt a variant of the BERT model, trained on large-scale abusive comments, using Masked Language Model (MLM) fine-tuning. Our evaluation shows that the UDA approaches result in sub-optimal performance, while the MLM fine-tuning does better in the cross-corpora setting. Detailed analysis reveals the limitations of the UDA approaches and emphasizes the need to build efficient adaptation methods for this task.
Fichier principal
Vignette du fichier
Social_NLP_NAACL.pdf (1.08 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03204605 , version 1 (21-04-2021)


  • HAL Id : hal-03204605 , version 1


Tulika Bose, Irina Illina, Dominique Fohr. Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection. SocialNLP 2021 - The 9th International Workshop on Natural Language Processing for Social Media, Jun 2021, Virtual, France. ⟨hal-03204605⟩
259 View
348 Download


Gmail Mastodon Facebook X LinkedIn More