Multiple Instance Learning for Offensive Language Detection
Jiexi Liu, Dehan Kong, Longtao Huang, Dinghui Mao, Hui Xue · 2022
Automatic offensive language detection has become a crucial issue in recent years.Existing researches on this topic are usually based on a large amount of data annotated at sentence level to train a robust model.However, sentence-level annotations are expensive in practice as the scenario expands, while there exist a large amount of natural labels from historical information on online platforms such as reports and punishments.Notably, these natural labels are usually in bag-level corresponding to the whole documents (articles, user profiles, conversations, etc.).Therefore, we target at proposing an approach capable of utilizing the bag-level labeled data for offensive language detection in this study.For this purpose, we formalize this task into a multiple instance learning (MIL) problem.We break down the design of existing MIL methods and propose a hybrid fusion MIL model with mutual-attention mechanism.In order to verify the validity of the proposed method, we present two new bag-level labeled datasets for offensive language detection: OLID-bags and MINOR.Experimental results based on the proposed datasets demonstrate the effectiveness of the mutual-attention method at both sentence level and bag level.WARNING: This paper contains tweet examples that could be offensive and biased.