Contextual Gating within the Transformer Stack: Synergistic Feature Modulation for Enhanced Lyrical Classification and Calibration

Abstract

This study introduces a significant architectural advancement in feature fusion for lyrical content classification by integrating auxiliary structural features directly into the self-attention mechanism of a pre-trained Transformer. I propose the SFL Transformer, a novel deep learning model that utilizes a Contextual Gating mechanism (an Intermediate SFL) to modulate the sequence of hidden states within the BERT encoder stack, rather than fusing features at the final output layer. This approach modulates the deep, contextualized semantic features (Hseq) using low-dimensional structural cues (Fstruct). The model is applied to a challenging binary classification task derived from UMAP-reduced lyrical embeddings. The SFL Transformer achieved an Accuracy of 0.9910 and a Macro F1 score of 0.9910, significantly improving the state-of-the-art established by the previously published SFL model (Accuracy 0.9894). Crucially, this Contextual Gating strategy maintained exceptional reliability, with a low Expected Calibration Error (ECE = 0.0081) and Log Loss (0.0489). This work validates the hypothesis that injecting auxiliary context mid-stack is the most effective means of synergistically combining structural and semantic information, creating a model with both superior discriminative power and high-fidelity probability estimates.