@inproceedings{satish-kumar-joshi-2026-ecoaffecttrack,
title = "{E}co{A}ffect{T}rack at {S}em{E}val-2026 Task 2: A Hierarchical {D}e{BERT}a-Transformer Framework with {CCC} Optimization for Longitudinal Affect Modeling",
author = "Satish Kumar, Diya and
Joshi, Om",
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-1.77/",
pages = "540--545",
ISBN = "979-8-89176-414-9",
abstract = "This submission proposes a hierarchical framework for longitudinal affect modeling, specifically designed for predicting variations in emotional valence and arousal over time. The system utilizes a DeBERTa-v3 encoder backbone optimized with a differentiable Concordance Correlation Coefficient (CCC) Loss for affect assessment (Subtask 1). This approach prioritizes capturing the ``shape'' and trend of emotional trajectories over absolute point-wise accuracy, yielding a significant performance gain over standard Mean Squared Error.For state change forecasting (Subtask 2A), the framework employs a Transformer-based temporal forecaster with positional encoding to account for inter-subject variability in emotional baselines. Disposition profiling (Subtask 2B) is addressed using a deep attention network that aggregates historical embeddings to identify emotionally informative essays. Experimental results from the official competition indicate that aligning the loss function with evaluation metrics and utilizing task-specific temporal modeling are essential for robust performance in longitudinal emotion recognition."
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<abstract>This submission proposes a hierarchical framework for longitudinal affect modeling, specifically designed for predicting variations in emotional valence and arousal over time. The system utilizes a DeBERTa-v3 encoder backbone optimized with a differentiable Concordance Correlation Coefficient (CCC) Loss for affect assessment (Subtask 1). This approach prioritizes capturing the “shape” and trend of emotional trajectories over absolute point-wise accuracy, yielding a significant performance gain over standard Mean Squared Error.For state change forecasting (Subtask 2A), the framework employs a Transformer-based temporal forecaster with positional encoding to account for inter-subject variability in emotional baselines. Disposition profiling (Subtask 2B) is addressed using a deep attention network that aggregates historical embeddings to identify emotionally informative essays. Experimental results from the official competition indicate that aligning the loss function with evaluation metrics and utilizing task-specific temporal modeling are essential for robust performance in longitudinal emotion recognition.</abstract>
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%0 Conference Proceedings
%T EcoAffectTrack at SemEval-2026 Task 2: A Hierarchical DeBERTa-Transformer Framework with CCC Optimization for Longitudinal Affect Modeling
%A Satish Kumar, Diya
%A Joshi, Om
%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 satish-kumar-joshi-2026-ecoaffecttrack
%X This submission proposes a hierarchical framework for longitudinal affect modeling, specifically designed for predicting variations in emotional valence and arousal over time. The system utilizes a DeBERTa-v3 encoder backbone optimized with a differentiable Concordance Correlation Coefficient (CCC) Loss for affect assessment (Subtask 1). This approach prioritizes capturing the “shape” and trend of emotional trajectories over absolute point-wise accuracy, yielding a significant performance gain over standard Mean Squared Error.For state change forecasting (Subtask 2A), the framework employs a Transformer-based temporal forecaster with positional encoding to account for inter-subject variability in emotional baselines. Disposition profiling (Subtask 2B) is addressed using a deep attention network that aggregates historical embeddings to identify emotionally informative essays. Experimental results from the official competition indicate that aligning the loss function with evaluation metrics and utilizing task-specific temporal modeling are essential for robust performance in longitudinal emotion recognition.
%U https://aclanthology.org/2026.semeval-1.77/
%P 540-545
Markdown (Informal)
[EcoAffectTrack at SemEval-2026 Task 2: A Hierarchical DeBERTa-Transformer Framework with CCC Optimization for Longitudinal Affect Modeling](https://aclanthology.org/2026.semeval-1.77/) (Satish Kumar & Joshi, SemEval 2026)
ACL