@inproceedings{lolli-etal-2026-es4mll,
title = "{ES}4{MLL} at {S}em{E}val-2026 Task 2: Set Attention Aggregation and Recurrent Temporal Modeling for Longitudinal Affect Prediction",
author = "Lolli, Andrea and
Lunazzi, Chiara and
Coppola, Riccardo and
Giobergia, Flavio",
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-1.102/",
pages = "720--726",
ISBN = "979-8-89176-414-9",
abstract = "Longitudinal modelling of affect from text requires capturing both linguistic content and temporal emotional dynamics. SemEval-2026 Task 2 introduces a dataset of essays and feeling words annotated with self-reported valence and arousal scores. In this work, we propose a neural architecture that combines pretrained Transformer encoders with temporal sequence modelling to predict continuous valence and arousal over user-specific timelines. Individual texts are encoded using a Transformer-based language model and aggregated through attention-based pooling before being processed by recurrent layers to capture longitudinal dependencies. To adapt pretrained representations under limited data conditions, we explore parameter-efficient fine-tuning strategies. We make the code available at https://github.com/AndreaLolli2912/SemEval2026-EmoVA."
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<abstract>Longitudinal modelling of affect from text requires capturing both linguistic content and temporal emotional dynamics. SemEval-2026 Task 2 introduces a dataset of essays and feeling words annotated with self-reported valence and arousal scores. In this work, we propose a neural architecture that combines pretrained Transformer encoders with temporal sequence modelling to predict continuous valence and arousal over user-specific timelines. Individual texts are encoded using a Transformer-based language model and aggregated through attention-based pooling before being processed by recurrent layers to capture longitudinal dependencies. To adapt pretrained representations under limited data conditions, we explore parameter-efficient fine-tuning strategies. We make the code available at https://github.com/AndreaLolli2912/SemEval2026-EmoVA.</abstract>
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%0 Conference Proceedings
%T ES4MLL at SemEval-2026 Task 2: Set Attention Aggregation and Recurrent Temporal Modeling for Longitudinal Affect Prediction
%A Lolli, Andrea
%A Lunazzi, Chiara
%A Coppola, Riccardo
%A Giobergia, Flavio
%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 lolli-etal-2026-es4mll
%X Longitudinal modelling of affect from text requires capturing both linguistic content and temporal emotional dynamics. SemEval-2026 Task 2 introduces a dataset of essays and feeling words annotated with self-reported valence and arousal scores. In this work, we propose a neural architecture that combines pretrained Transformer encoders with temporal sequence modelling to predict continuous valence and arousal over user-specific timelines. Individual texts are encoded using a Transformer-based language model and aggregated through attention-based pooling before being processed by recurrent layers to capture longitudinal dependencies. To adapt pretrained representations under limited data conditions, we explore parameter-efficient fine-tuning strategies. We make the code available at https://github.com/AndreaLolli2912/SemEval2026-EmoVA.
%U https://aclanthology.org/2026.semeval-1.102/
%P 720-726
Markdown (Informal)
[ES4MLL at SemEval-2026 Task 2: Set Attention Aggregation and Recurrent Temporal Modeling for Longitudinal Affect Prediction](https://aclanthology.org/2026.semeval-1.102/) (Lolli et al., SemEval 2026)
ACL