@inproceedings{le-etal-2026-vap,
title = "{VAP}-{G}ame{C}ontroller at {S}em{E}val-2026 Task 2: Lexical-based and Emotion-Aware Approaches for Longtitudinal Emotion Prediction",
author = "Le, Huy and
Phu, Truong and
Tran, Trung and
Nguyen, Nga and
Choudhury, Monojit",
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-1.37/",
pages = "256--262",
ISBN = "979-8-89176-414-9",
abstract = "In this work, we participate in SemEval-2026 Task 2, which focuses on predicting continuous valence and arousal trajectories from longitudinal ecological essays. To model fine-grained emotional dynamics, we explore three approaches: (1) hierarchical encoder-based models to capture contextual emotional patterns, (2) a lexicon-based pipeline with linguistic rules and a dual-level calibration mechanismfor personalized estimation, and (3) a hybrid framework that integrates lexical emotional signals into neural encoders. Experiments on the official dataset, evaluated using Pearson correlation (r) and MAE, show consistent improvements over baseline methods, highlighting the complementary strengths of neural representations and calibrated lexical features."
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<abstract>In this work, we participate in SemEval-2026 Task 2, which focuses on predicting continuous valence and arousal trajectories from longitudinal ecological essays. To model fine-grained emotional dynamics, we explore three approaches: (1) hierarchical encoder-based models to capture contextual emotional patterns, (2) a lexicon-based pipeline with linguistic rules and a dual-level calibration mechanismfor personalized estimation, and (3) a hybrid framework that integrates lexical emotional signals into neural encoders. Experiments on the official dataset, evaluated using Pearson correlation (r) and MAE, show consistent improvements over baseline methods, highlighting the complementary strengths of neural representations and calibrated lexical features.</abstract>
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%0 Conference Proceedings
%T VAP-GameController at SemEval-2026 Task 2: Lexical-based and Emotion-Aware Approaches for Longtitudinal Emotion Prediction
%A Le, Huy
%A Phu, Truong
%A Tran, Trung
%A Nguyen, Nga
%A Choudhury, Monojit
%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 le-etal-2026-vap
%X In this work, we participate in SemEval-2026 Task 2, which focuses on predicting continuous valence and arousal trajectories from longitudinal ecological essays. To model fine-grained emotional dynamics, we explore three approaches: (1) hierarchical encoder-based models to capture contextual emotional patterns, (2) a lexicon-based pipeline with linguistic rules and a dual-level calibration mechanismfor personalized estimation, and (3) a hybrid framework that integrates lexical emotional signals into neural encoders. Experiments on the official dataset, evaluated using Pearson correlation (r) and MAE, show consistent improvements over baseline methods, highlighting the complementary strengths of neural representations and calibrated lexical features.
%U https://aclanthology.org/2026.semeval-1.37/
%P 256-262
Markdown (Informal)
[VAP-GameController at SemEval-2026 Task 2: Lexical-based and Emotion-Aware Approaches for Longtitudinal Emotion Prediction](https://aclanthology.org/2026.semeval-1.37/) (Le et al., SemEval 2026)
ACL