用讽刺语料预训练，提升隐性仇恨言论检测效果
Transfer Learning via Lexical Relatedness: A Sarcasm and Hate Speech Case Study
- 用讽刺文本预训练模型，再迁移到仇恨言论任务
- BERT+BiLSTM在ETHOS数据集上召回率提升9.7%
- 适合研究隐性仇恨言论检测与迁移学习的读者
识别非直接形式的仇恨言论（如反讽、讽刺和暗示）仍是社交网络的重大挑战。尽管讽刺与仇恨言论被视为不同表达，本文探究将讽刺作为预训练步骤是否能提升隐性及显性仇恨言论检测能力。基于ETHOS、Sarcasm on Reddit和Implicit Hate Corpus数据集，设计两种训练策略：单步法（仅用讽刺数据训练后测试仇恨言论）与多阶段迁移学习（依次微调讽刺、隐性仇恨、显性仇恨）。结果表明，讽刺预训练使BERT+BiLSTM在ETHOS上的召回率提高9.7%，AUC提升7.8%，F1-score提升6%；在隐性仇恨语料上，精确率提升7.8%。实证显示，融入讽刺训练可显著增强模型对隐性和显性仇恨言论的检测能力。
原文摘要 · Abstract (English)
Detecting hate speech in non-direct forms, such as irony, sarcasm, and innuendos, remains a persistent challenge for social networks. Although sarcasm and hate speech are regarded as distinct expressions, our work explores whether integrating sarcasm as a pre-training step improves implicit hate speech detection and, by extension, explicit hate speech detection. Incorporating samples from ETHOS, Sarcasm on Reddit, and Implicit Hate Corpus, we devised two training strategies to compare the effectiveness of sarcasm pre-training on a CNN+LSTM and BERT+BiLSTM model. The first strategy is a single-step training approach, where a model trained only on sarcasm is then tested on hate speech. The second strategy uses sequential transfer learning to fine-tune models for sarcasm, implicit hate, and explicit hate. Our results show that sarcasm pre-training improved the BERT+BiLSTM's recall by 9.7%, AUC by 7.8%, and F1-score by 6% on ETHOS. On the Implicit Hate Corpus, precision increased by 7.8% when tested only on implicit samples. By incorporating sarcasm into the training process, we show that models can more effectively detect both implicit and explicit hate.
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