Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis
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TL;DR — CAVE-ABSA is a framework for generating and rigorously validating aspect-level counterfactuals for Aspect-Based Sentiment Analysis (ABSA), aiming to build reliable datasets for robustness testing and data augmentation. It matters because it exposes whether ABSA models truly reason about aspect-grounded sentiment rather than sentence-level polarity.
- Targets the ABSA-specific challenge: a valid counterfactual must flip one target aspect's sentiment while preserving other aspects, meaning, fluency, and factual consistency.
- Pipeline: localize the opinion span for the target aspect, perform controlled counterfactual rewriting, refine via a repair module, then filter candidates.
- Validation uses multiple explicit constraints — aspect-level verification, semantic similarity, AMR-guided structural preservation, edit minimality, fluency, and contradiction detection.
- Key design choice is separating generation from validation, addressing prior methods that produce fluent but aspect-invalid or semantically drifting edits.
Note: This is an abstract-only summary; no empirical results or benchmarks are reported in the provided content.
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Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis
TL;DR — CAVE-ABSA is a framework for generating and rigorously validating aspect-level counterfactuals for Aspect-Based Sentiment Analysis (ABSA), aiming to build reliable datasets for robustness testing and data augmentation. It matters because it exposes whether ABSA models truly reason about aspect-grounded sentiment rather than sentence-level polarity.
- Targets the ABSA-specific challenge: a valid counterfactual must flip one target aspect's sentiment while preserving other aspects, meaning, fluency, and factual consistency.
- Pipeline: localize the opinion span for the target aspect, perform controlled counterfactual rewriting, refine via a repair module, then filter candidates.
- Validation uses multiple explicit constraints — aspect-level verification, semantic similarity, AMR-guided structural preservation, edit minimality, fluency, and contradiction detection.
- Key design choice is separating generation from validation, addressing prior methods that produce fluent but aspect-invalid or semantically drifting edits.
Note: This is an abstract-only summary; no empirical results or benchmarks are reported in the provided content.