@inproceedings{chen-etal-2026-csiro,
title = "{CSIRO}-{LT} at {S}em{E}val-2026 Task 2: In-the-Wild Valence and Arousal Forecasting on Ecological Text Time Series",
author = {Chen, Jiyu and
B{\"o}l{\"u}c{\"u}, Necva and
Karimi, Sarvnaz and
Molla, Diego and
Paris, Cecile},
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-1.24/",
pages = "160--166",
ISBN = "979-8-89176-414-9",
abstract = "Predicting emotional valence and arousal in text is challenging due to the continuous, dynamic, and context-dependent nature of emotions. The SemEval 2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays shared task investigates longitudinal affect prediction from real-world personal essays, including forecasting short-term state and longer-term dispositional changes. We compare Pre-trained Language Models (PLMs) and Large Language Models (LLMs) for these subtasks, examining different input representations and feature formulations. We show that sentiment-aware PLMs are most effective for continuous valence and arousal prediction, and LLMs are effective for short-term state forecasting. Modelling dispositional changes remains challenging, and none of our neural approaches surpass simple a historical baseline approach in this setting."
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<abstract>Predicting emotional valence and arousal in text is challenging due to the continuous, dynamic, and context-dependent nature of emotions. The SemEval 2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays shared task investigates longitudinal affect prediction from real-world personal essays, including forecasting short-term state and longer-term dispositional changes. We compare Pre-trained Language Models (PLMs) and Large Language Models (LLMs) for these subtasks, examining different input representations and feature formulations. We show that sentiment-aware PLMs are most effective for continuous valence and arousal prediction, and LLMs are effective for short-term state forecasting. Modelling dispositional changes remains challenging, and none of our neural approaches surpass simple a historical baseline approach in this setting.</abstract>
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%0 Conference Proceedings
%T CSIRO-LT at SemEval-2026 Task 2: In-the-Wild Valence and Arousal Forecasting on Ecological Text Time Series
%A Chen, Jiyu
%A Bölücü, Necva
%A Karimi, Sarvnaz
%A Molla, Diego
%A Paris, Cecile
%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 chen-etal-2026-csiro
%X Predicting emotional valence and arousal in text is challenging due to the continuous, dynamic, and context-dependent nature of emotions. The SemEval 2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays shared task investigates longitudinal affect prediction from real-world personal essays, including forecasting short-term state and longer-term dispositional changes. We compare Pre-trained Language Models (PLMs) and Large Language Models (LLMs) for these subtasks, examining different input representations and feature formulations. We show that sentiment-aware PLMs are most effective for continuous valence and arousal prediction, and LLMs are effective for short-term state forecasting. Modelling dispositional changes remains challenging, and none of our neural approaches surpass simple a historical baseline approach in this setting.
%U https://aclanthology.org/2026.semeval-1.24/
%P 160-166
Markdown (Informal)
[CSIRO-LT at SemEval-2026 Task 2: In-the-Wild Valence and Arousal Forecasting on Ecological Text Time Series](https://aclanthology.org/2026.semeval-1.24/) (Chen et al., SemEval 2026)
ACL