@inproceedings{nerujan-sarveswaran-2026-feature,
    title = "A Feature-Fusion Ensemble Approach for {T}amil Hate Speech Detection",
    author = "Nerujan, Sathasivam  and
      Sarveswaran, Kengatharaiyer",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini",
    booktitle = "Proceedings of the Second workshop on Challenges in Processing {S}outh {A}sian Languages ({CH}i{PSAL}2026)",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.chipsal-1.18/",
    doi = "10.63317/2us8ubrf4jvi",
    pages = "190--197",
    abstract = "Detecting online toxicity in morphologically rich, low-resource languages like Tamil remains a major computational challenge. Standard transformer models often struggle with sub-word fragmentation, which can dilute the semantic intensity of regional insults and out-of-vocabulary slang. To mitigate this limitation, we train a multi-layer hybrid framework that fuses the deep contextual representations of L3Cube-TamilBERT with the character-level robustness of FastText embeddings. Our architecture leverages Last-4 Layers averaging and a dual pooling strategy (Mean + Max) to capture both global sentence intent and extract high-activation spikes of offensive cues typically lost in single layer representations. Experiments show that this hybrid model achieves a Macro-F1 of 0.7883, notably enhancing Hate Recall (0.7503) for detection of offensive content. Additionally, as reported by other studies, stacking ensemble achieves peak hate precision (0.9296), providing a high accuracy alternative for moderation scenarios requiring minimal false positives. By combining deep contextual hidden states with FastText embeddings, the proposed feature-fusion ensemble approach with multi-layer hybrid framework approach establishes a new benchmark for hate speech detection for Tamil."
}