ES4MLL at SemEval-2026 Task 2: Set Attention Aggregation and Recurrent Temporal Modeling for Longitudinal Affect Prediction
Year: 2026
Type: conference-paper
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 finetuning strategies.We make the code available at https://github.com/AndreaLolli2912/ SemEval2026-EmoVA.
Institution: Politecnico di Torino
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166832119