MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles
CoRR(2024)
摘要
The automatic identification of offensive language such as hate speech is
important to keep discussions civil in online communities. Identifying hate
speech in multimodal content is a particularly challenging task because
offensiveness can be manifested in either words or images or a juxtaposition of
the two. This paper presents the MasonPerplexity submission for the Shared Task
on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task
is divided into two sub-tasks: sub-task A focuses on the identification of hate
speech and sub-task B focuses on the identification of targets in text-embedded
images during political events. We use an XLM-roBERTa-large model for sub-task
A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and
BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A
and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.
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