SCUMesclab at SemEval-2026 Task 3: An Adaptive Dual-Track Framework for Dimensional Aspect-Based Sentiment Analysis

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

SCUMesclab's system for SemEval-2026 Task 3 addresses the challenge of predicting continuous valence and arousal scores, a task complicated by varying data scales and cross-lingual pragmatic ambiguities. The team developed an Adaptive Dual-Track Framework that dynamically selects modeling strategies based on specific task characteristics. For semantically stable tasks, the framework employs a robust single baseline optimized with layer-wise learning rate decay (LLRD) to ensure stability. In contrast, high-ambiguity scenarios, such as those found in the Environmental Protection domain, utilize a heterogeneous ensemble strategy to mitigate prediction variance. Experimental results confirm that this lightweight approach consistently outperforms the initial standard baseline across all subtasks, demonstrating remarkable parameter efficiency and achieving competitive performance against newly introduced large language model (LLM) baselines. Ablation studies further indicate that conventional regularization, cross-lingual data transfer, and homogeneous ensemble learning can lead to negative transfer in regression settings, underscoring the necessity of linguistically tailored strategies.

Key takeaway

For NLP Engineers designing dimensional aspect-based sentiment analysis systems, especially across diverse languages or domains, you should critically evaluate generic modeling strategies. Your approach must dynamically adapt to task ambiguity. Employ techniques like layer-wise learning rate decay for stable tasks and heterogeneous ensembles for high-ambiguity scenarios. Blindly applying conventional regularization or cross-lingual data transfer in regression settings can lead to negative transfer, hindering your system's performance. Prioritize tailored strategies over one-size-fits-all solutions.

Key insights

Adapting modeling strategies based on task ambiguity is crucial for accurate dimensional sentiment analysis.

Principles

• Modeling strategies must dynamically adapt to task characteristics.
• Conventional regularization and cross-lingual transfer can cause negative transfer in regression tasks.

Method

An Adaptive Dual-Track Framework dynamically selects between a robust single baseline with layer-wise learning rate decay (LLRD) for stable tasks and a heterogeneous ensemble for high-ambiguity scenarios.

In practice

• Apply LLRD for stable sentiment regression tasks.
• Utilize heterogeneous ensembles for ambiguous domains like Environmental Protection.
• Re-evaluate conventional regularization for regression settings.

Topics

• Dimensional Sentiment Analysis
• Adaptive Frameworks
• SemEval-2026
• Ensemble Learning
• Layer-wise Learning Rate Decay
• Negative Transfer

Best for: AI Engineer, Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.