CITD@UIT at SemEval-2026 Task 2: Temporal Mixture-of-Experts for Longitudinal Valence and Arousal Prediction from Ecological Essays
Year: 2026
Type: conference-paper
Abstract: This paper describes our participation in SemEval-2026 Task 2, which focuses on the longitudinal assessment and forecasting of emotional states through text.The challenge is divided into two primary objectives: Subtask 1, which requires estimating continuous Valence and Arousal (V&A) scores for a sequence of texts, and Subtask 2, which focuses on forecasting future emotional variations, specifically State Change (2A) and Dispositional Change (2B).To address these tasks, we propose a unified framework based on cardiffnlp/ twitter-roberta-base-sentiment-latest 1 , a transformer architecture pretrained on 124 million tweets.For all subtasks, we sort the data chronologically by user_id and use a sliding window approach to capture longitudinal context.We conduct extensive experiments combining this pretrained RoBERTa model with Multilayer Perceptron (MLP) and Mixture-of-Experts (MoE) architectures to optimize performance.Furthermore, we utilize both attention pooling and mean pooling on all output hidden state representations to extract richer semantic features.Our proposed system demonstrated competitive performance, officially ranking 9th in Subtask 1 and 5th in Subtask 2A among participating teams. 2
Authors: Son Phuong, My Ngo, Tri Minh Dao, Duc-Vu Nguyen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166857357