This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chronologically ordered user-generated texts. We explore three complementary approaches: (1) LLM prompting under user-aware and user-agnostic settings, (2) a pairwise Maximum Entropy (MaxEnt) model with Ising-style interactions for structured transition modeling, and (3) a lightweight neural regression model incorporating recent affective trajectories and trainable user embeddings. Our findings indicate that LLMs effectively capture static affective signals from text, whereas short-term affective variation in this dataset is more strongly explained by recent numeric state trajectories than by textual semantics. Our system ranked first among participating teams in both Subtask 1 and Subtask 2A based on the official evaluation metric.
@article{arxiv.2604.21534,
title = {UKP_Psycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text},
author = {Darya Hryhoryeva and Amaia Zurinaga and Hamidreza Jamalabadi and Iryna Gurevych},
journal= {arXiv preprint arXiv:2604.21534},
year = {2026}
}
Comments
Accepted to SemEval 2026 (co-located with ACL 2026)