NLP-FSDM at SemEval-2026 Task 2: Temporal Smoothing and CCC-MAE Optimization for Balanced Longitudinal Affect Assessment

AbstractThis paper describes the NLP-FSDM system for SemEval-2026 Task 2, Subtask 1 on longitudinal affect assessment. The task requires predicting Valence and Arousal (V & A) scores for sequences of ecological essays and feeling words written over time. We adopt ModernBERT-large as a text encoder and formulate the task as a joint regression problem optimized using a Concordance Correlation Coefficient (CCC) loss combined with a lightly weighted Mean Absolute Error (MAE) term. To reduce variance induced by fine-tuning large transformers on relatively small user-specific datasets, we employ a three-seed ensemble. Finally, we introduce a lightweight post-inference temporal smoothing mechanism applied per user to improve within-user consistency. Our system achieves an rcomposite of 0.546 for Valence and 0.453 for Arousal, demonstrating stable cross-dimensional performance without explicitly modeling sequential dependencies.