Interpreting Text-Enriched Dual-Head Multitask Learning for Indonesian Hateful Meme Detection Using Explainable AI - Riadi, Selamet; Emi Suryadi; Muhamad Masjun Efendi; Bahtiar Imran; Muhammad Zamroni Uska (Jurnal Kecerdasan Buatan dan Teknologi Informasi, 2026)
Internet memes in Indonesia are frequently weaponized to disseminate implicit hate speech through sarcasm and cultural nuances. Automatically detecting such content is computationally challenging, and existing deep learning frameworks predominantly operate as opaque black boxes, lacking decision transparency. This study implements and optimizes a text-enriched dual-head multitask learning architecture utilizing IndoBERTweet to concurrently classify hatefulness and appropriateness within the INDOMEME dataset. Rather than processing raw image pixels, we employ a text-enrichment strategy where visual semantics are transcribed into textual descriptors via Optical Character Recognition and vision-language captioning. To bridge the interpretability gap, we deploy Local Interpretable Model-agnostic Explanations (LIME) to decode the internal feature attributions of the architecture. Furthermore, advanced training optimizations, encompassing cosine annealing, gradient accumulation, class-weighted loss, and dynamic threshold calibration, were engineered to enhance model generalization. Experimental evaluations demonstrate that the optimized model achieves a Macro-F1 score of 0.812 for hatefulness and 0.820 for appropriateness, surpassing the established baseline. Crucially, the LIME analysis unveils a pivotal finding: despite sharing an identical textual backbone, the hate-specific head predominantly focuses on lexicons carrying social agitation, whereas the appropriateness head prioritizes general norm violations. These empirical findings substantiate that multitask learning enriches semantic representation quality, offering a transparent framework for trustworthy content moderation.