Towards Weakly-Supervised Hate Speech Classification Across Datasets

Autor:	Jin, Yiping, Wanner, Leo, Kadam, Vishakha Laxman, Shvets, Alexander
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata. Subsequently, supervised-learning models tend to generalize poorly to datasets they were not trained on, and the performance of the models trained on datasets labeled using different HS taxonomies cannot be compared. To ease this problem, we propose applying extremely weak supervision that only relies on the class name rather than on class samples from the annotated data. We demonstrate the effectiveness of a state-of-the-art weakly-supervised text classification model in various in-dataset and cross-dataset settings. Furthermore, we conduct an in-depth quantitative and qualitative analysis of the source of poor generalizability of HS classification models. Comment: WOAH 7@ACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.02637 Zobrazit plný text záznamu View this record from Arxiv