BertKittens at SemEval-2026 Task 3: Multi-Domain Aspect Sentiment with BERT/DeBERTa Ensembles for VA Regression and Aspect–Opinion–VA Triplets
Year: 2026
Type: conference-paper
Abstract: We describe our submission to SemEval-2026 Task 3 (Track A) (Yu et al., 2026), which focuses on quantitative tone assessment and aspect-based opinion extraction.Our system is built on transformer encoders (BERT and DeBERTa) fine-tuned in a multitask learning framework.For the regression subtask (evaluated with RMSE), we jointly predict Valence-Arousal (VA) scores and tokenlevel opinion spans using a shared encoder with task-specific output heads.This formulation introduces auxiliary supervision at the token level, which stabilizes training and improves regression accuracy compared to single-task optimization.When gold abstracts and opinion annotations are provided, our models achieve strong performance.However, in fully end-to-end settings requiring automatic span extraction, performance degrades substantially due to error propagation from token-level predictions.Our findings highlight the benefits of joint affective regression and span modeling, while exposing the limitations of transformer-based sequence labeling under strict end-to-end evaluation constraints.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166835551