LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysis

1. 1Identify the prediction difficulty of Valence and Arousal scores across languages and domains
2. 2Use uncertainty-weighted multitask learning to address the differing prediction difficulties
3. 3Implement a system like LogSigma to predict continuous Valence and Arousal scores
4. 4Evaluate the system on a task like SemEval-2026 Task 3 to assess its performance

NLP researchers and engineers on a team can benefit from this approach to improve sentiment analysis models, especially when dealing with continuous sentiment scores

💡 Uncertainty-weighted multitask learning can improve the performance of sentiment analysis models by addressing differing prediction difficulties across languages and domains

💡 Uncertainty-weighted multitask learning for dimensional aspect-based sentiment analysis