Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection

COLING 2022  ·  Jiyun Kim, Byounghan Lee, Kyung-Ah Sohn ·

In a hate speech detection model, we should consider two critical aspects in addition to detection performance-bias and explainability. Hate speech cannot be identified based solely on the presence of specific words: the model should be able to reason like humans and be explainable. To improve the performance concerning the two aspects, we propose Masked Rationale Prediction (MRP) as an intermediate task. MRP is a task to predict the masked human rationales-snippets of a sentence that are grounds for human judgment-by referring to surrounding tokens combined with their unmasked rationales. As the model learns its reasoning ability based on rationales by MRP, it performs hate speech detection robustly in terms of bias and explainability. The proposed method generally achieves state-of-the-art performance in various metrics, demonstrating its effectiveness for hate speech detection.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Hate Speech Detection HateXplain BERT-MRP AUROC 0.862 # 1
Accuracy 0.704 # 2
Macro F1 0.699 # 1
Hate Speech Detection HateXplain BERT-RP AUROC 0.853 # 2
Accuracy 0.707 # 1
Macro F1 0.693 # 2


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