@inproceedings{nadiger-etal-2026-momentum,
title = "Momentum at {S}em{E}val-2026 Task 2: {L}ong{VA}-{R}o{BERT}a, a transformer-Based Longitudinal Valence and Arousal Modeling",
author = "Nadiger, Supriya and
Saumya, Sunil and
Pujari, Rahul and
Hiremath, Veeresh and
Chikaraddi, Kiran and
Kadkol, Anoop",
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-1.78/",
pages = "546--552",
ISBN = "979-8-89176-414-9",
abstract = "This paper studies the emotion as affective circumplex model representing valence and arousal in continuous two dimensional space. It also explores the disposition of emotion over time to identify the behavioural cues and self-identified affective states. while traditional methods use categorical emotion classes, SemEval 2026 Task 2 studies emotions in continuous space. In this paper, we proposes a transformer-based LongVA-RoBERTa model for emotion modeling in regression for ecological essays. For subtask 1 , we develop an affect prediction framework employing RoBERTa with attention pooling and a regression head for valence and arousal prediction. In subtask 2A , we employ BiLSTM to capture the temporal dependencies and fuse surface, contextual, user-level features to predict short-term affect variation. Our results outperform the baseline, paving ways to continue emotion prediction in continuous dimensional space"
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<abstract>This paper studies the emotion as affective circumplex model representing valence and arousal in continuous two dimensional space. It also explores the disposition of emotion over time to identify the behavioural cues and self-identified affective states. while traditional methods use categorical emotion classes, SemEval 2026 Task 2 studies emotions in continuous space. In this paper, we proposes a transformer-based LongVA-RoBERTa model for emotion modeling in regression for ecological essays. For subtask 1 , we develop an affect prediction framework employing RoBERTa with attention pooling and a regression head for valence and arousal prediction. In subtask 2A , we employ BiLSTM to capture the temporal dependencies and fuse surface, contextual, user-level features to predict short-term affect variation. Our results outperform the baseline, paving ways to continue emotion prediction in continuous dimensional space</abstract>
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%0 Conference Proceedings
%T Momentum at SemEval-2026 Task 2: LongVA-RoBERTa, a transformer-Based Longitudinal Valence and Arousal Modeling
%A Nadiger, Supriya
%A Saumya, Sunil
%A Pujari, Rahul
%A Hiremath, Veeresh
%A Chikaraddi, Kiran
%A Kadkol, Anoop
%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 nadiger-etal-2026-momentum
%X This paper studies the emotion as affective circumplex model representing valence and arousal in continuous two dimensional space. It also explores the disposition of emotion over time to identify the behavioural cues and self-identified affective states. while traditional methods use categorical emotion classes, SemEval 2026 Task 2 studies emotions in continuous space. In this paper, we proposes a transformer-based LongVA-RoBERTa model for emotion modeling in regression for ecological essays. For subtask 1 , we develop an affect prediction framework employing RoBERTa with attention pooling and a regression head for valence and arousal prediction. In subtask 2A , we employ BiLSTM to capture the temporal dependencies and fuse surface, contextual, user-level features to predict short-term affect variation. Our results outperform the baseline, paving ways to continue emotion prediction in continuous dimensional space
%U https://aclanthology.org/2026.semeval-1.78/
%P 546-552
Markdown (Informal)
[Momentum at SemEval-2026 Task 2: LongVA-RoBERTa, a transformer-Based Longitudinal Valence and Arousal Modeling](https://aclanthology.org/2026.semeval-1.78/) (Nadiger et al., SemEval 2026)
ACL
- Supriya Nadiger, Sunil Saumya, Rahul Pujari, Veeresh Hiremath, Kiran Chikaraddi, and Anoop Kadkol. 2026. Momentum at SemEval-2026 Task 2: LongVA-RoBERTa, a transformer-Based Longitudinal Valence and Arousal Modeling. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 546–552, San Diego, California, USA. Association for Computational Linguistics.