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    <abstract>This paper describes our system for SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal. We approached the task by fine-tuning a weighted ensemble of DeBERTa-v3-base models. Our system achieved the second-highest Valence composite correlation and ranked 5th in the overall V&amp;A average in Subtask 1. More importantly, we provide an empirical analysis of our model’s performance on longitudinal tasks, where it exhibited significant inverse cor- relations. We quantify the Venting Effect, showing a systematic tendency for the model to over-index on negative lexical cues despite self-reported relief. Furthermore, we analyze the structural trade-off between Mean Absolute Error and Pearson correlation induced by smoothing techniques.</abstract>
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%0 Conference Proceedings
%T lamanhnguyen at SemEval-2026 Task 2: Uncovering Lexical Bias and Momentum Lag in Longitudinal Emotion Prediction using Multi-task DeBERTa
%A Nguyen, Lam Anh
%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 nguyen-2026-lamanhnguyen
%X This paper describes our system for SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal. We approached the task by fine-tuning a weighted ensemble of DeBERTa-v3-base models. Our system achieved the second-highest Valence composite correlation and ranked 5th in the overall V&A average in Subtask 1. More importantly, we provide an empirical analysis of our model’s performance on longitudinal tasks, where it exhibited significant inverse cor- relations. We quantify the Venting Effect, showing a systematic tendency for the model to over-index on negative lexical cues despite self-reported relief. Furthermore, we analyze the structural trade-off between Mean Absolute Error and Pearson correlation induced by smoothing techniques.
%U https://aclanthology.org/2026.semeval-1.91/
%P 630-634

Markdown (Informal)

[lamanhnguyen at SemEval-2026 Task 2: Uncovering Lexical Bias and Momentum Lag in Longitudinal Emotion Prediction using Multi-task DeBERTa](https://aclanthology.org/2026.semeval-1.91/) (Nguyen, SemEval 2026)

• lamanhnguyen at SemEval-2026 Task 2: Uncovering Lexical Bias and Momentum Lag in Longitudinal Emotion Prediction using Multi-task DeBERTa (Nguyen, SemEval 2026)

ACL

• Lam Anh Nguyen. 2026. lamanhnguyen at SemEval-2026 Task 2: Uncovering Lexical Bias and Momentum Lag in Longitudinal Emotion Prediction using Multi-task DeBERTa. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 630–634, San Diego, California, USA. Association for Computational Linguistics.