@inproceedings{shah-etal-2026-ai4pc,
title = "{AI}4{PC}-{H}oward {U}niversity at {S}em{E}val-2026 Task 2: Fine-Tuning {D}istil{BERT}, {D}e{BERT}a and {M}odern{BERT} for Valence{--}Arousal Prediction and Change Estimation",
author = "Shah, Araj and
Shah, Utsav and
Aryal, Saurav",
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-1.89/",
pages = "617--623",
ISBN = "979-8-89176-414-9",
abstract = "We present lightweight, reproducible models for longitudinal valence{--}arousal (VA) prediction in the SemEval-2026 Task 2 essay corpus. Using only the official data, we enforce user-disjoint splits to prevent leakage and evaluate three settings: essay-level VA state estimation, short-horizon VA change forecasting, and long-horizon disposition change prediction. Our submitted systems use DistilBERT for essay-level regression, ModernBERT-based history modeling with a GRU and a blended previous-delta baseline for short-horizon change, and pooled DeBERTa history embeddings with a compact MLP for disposition change. On the official evaluation, across our best performing approaches, we achieve rcomp =0.665/0.468 (valence/arousal) for Subtask 1, r = 0.597/0.413 for Subtask 2A, and r =0.046/0.348 for Subtask 2B."
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<title>AI4PC-Howard University at SemEval-2026 Task 2: Fine-Tuning DistilBERT, DeBERTa and ModernBERT for Valence–Arousal Prediction and Change Estimation</title>
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<abstract>We present lightweight, reproducible models for longitudinal valence–arousal (VA) prediction in the SemEval-2026 Task 2 essay corpus. Using only the official data, we enforce user-disjoint splits to prevent leakage and evaluate three settings: essay-level VA state estimation, short-horizon VA change forecasting, and long-horizon disposition change prediction. Our submitted systems use DistilBERT for essay-level regression, ModernBERT-based history modeling with a GRU and a blended previous-delta baseline for short-horizon change, and pooled DeBERTa history embeddings with a compact MLP for disposition change. On the official evaluation, across our best performing approaches, we achieve rcomp =0.665/0.468 (valence/arousal) for Subtask 1, r = 0.597/0.413 for Subtask 2A, and r =0.046/0.348 for Subtask 2B.</abstract>
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%0 Conference Proceedings
%T AI4PC-Howard University at SemEval-2026 Task 2: Fine-Tuning DistilBERT, DeBERTa and ModernBERT for Valence–Arousal Prediction and Change Estimation
%A Shah, Araj
%A Shah, Utsav
%A Aryal, Saurav
%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 shah-etal-2026-ai4pc
%X We present lightweight, reproducible models for longitudinal valence–arousal (VA) prediction in the SemEval-2026 Task 2 essay corpus. Using only the official data, we enforce user-disjoint splits to prevent leakage and evaluate three settings: essay-level VA state estimation, short-horizon VA change forecasting, and long-horizon disposition change prediction. Our submitted systems use DistilBERT for essay-level regression, ModernBERT-based history modeling with a GRU and a blended previous-delta baseline for short-horizon change, and pooled DeBERTa history embeddings with a compact MLP for disposition change. On the official evaluation, across our best performing approaches, we achieve rcomp =0.665/0.468 (valence/arousal) for Subtask 1, r = 0.597/0.413 for Subtask 2A, and r =0.046/0.348 for Subtask 2B.
%U https://aclanthology.org/2026.semeval-1.89/
%P 617-623
Markdown (Informal)
[AI4PC-Howard University at SemEval-2026 Task 2: Fine-Tuning DistilBERT, DeBERTa and ModernBERT for Valence–Arousal Prediction and Change Estimation](https://aclanthology.org/2026.semeval-1.89/) (Shah et al., SemEval 2026)
ACL