@inproceedings{hryhoryeva-etal-2026-ukppsycontrol,
title = "{UKPP}sycontrol at {S}em{E}val-2026 Task 2: Modeling Valence and Arousal Dynamics from Text",
author = "Hryhoryeva, Darya and
Zurinaga, Amaia and
Jamalabadi, Hamidreza and
Gurevych, Iryna",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.76/",
pages = "528--539",
ISBN = "979-8-89176-414-9",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T UKPPsycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text
%A Hryhoryeva, Darya
%A Zurinaga, Amaia
%A Jamalabadi, Hamidreza
%A Gurevych, Iryna
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F hryhoryeva-etal-2026-ukppsycontrol
%X 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.
%U https://aclanthology.org/2026.semeval-1.76/
%P 528-539
Markdown (Informal)
[UKPPsycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text](https://aclanthology.org/2026.semeval-1.76/) (Hryhoryeva et al., SemEval 2026)
ACL