A Multitask Learning Framework for Abuse Detection and Emotion Classification

Abstract

The rapid development of online social media makes abuse detection a hot topic in the field of emotional computing. However, most natural language processing (NLP) methods only focus on linguistic features of posts and ignore the influence of users’ emotions. To tackle the problem, we propose a multitask framework combining abuse detection and emotion classification (MFAE) to expand the representation capability of the algorithm on the basis of the existing pretrained language model. Specifically, we use bidirectional encoder representation from transformers (BERT) as the encoder to generate sentence representation. Then, we used two different decoders for emotion classification and abuse detection, respectively. To further strengthen the influence of the emotion classification task on abuse detection, we propose a cross-attention (CA) component in the decoder, which further improves the learning effect of our multitask learning framework. Experimental results on five public datasets show that our method is superior to other state-of-the-art methods.