"AGI” Team at SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays
Year: 2026
Type: conference-paper
Abstract: This paper describes our submission to SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal.We combine RoBERTa-Large text encoding with a unidirectional GRU for temporal modeling and gated user embeddings for personalization.A four-phase staged training curriculum employs ordinal regression for absolute affect prediction and a zero-inflated delta model for change detection.Our approach achieves competitive performance on Subtask 1 (longitudinal affect assessment) with composite correlation r = 0.600 for valence and r = 0.452 for arousal.However, we observe systematic degradation in Subtask 2A (state change detection) with negative correlations (r = -0.167for valence, r = -0.147for arousal), revealing a fundamental trade-off between stability-oriented representations and change sensitivity.We provide detailed empirical analysis of these failure modes, contributing insights into the challenges of modeling emotional dynamics in ecological data.Code and trained checkpoints are publicly available.1,2
Author: Harsh Rathva
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166800371