A Feature-Fusion Ensemble Approach for Tamil Hate Speech Detection

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 newbenchmark for hate speech detection for Tamil.

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