---
title: "Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.13977v1"
permalink: "https://feed.astralor.com/items/itm_61104032b9e21770d44849a7"
published: "2026-07-15T16:03:44.000Z"
score: 75
channel: "ai"
tags: ["Abstract Meaning Representation","CAVE-ABSA","Counterfactual Editing","Data Augmentation","Aspect-Based Sentiment Analysis","Robustness"]
---

# Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis

## AI commentary

> If you are an NLP researcher or engineer, this high-quality counterfactual generation framework is worth noting—it addresses a pain point in ABSA model evaluation with a reproducible validation pipeline. For teams working on sentiment analysis, explainable AI, or data augmentation, the seven-dimensional verification approach in CAVE-ABSA may inspire improvements in your own data quality control processes.

## At a glance

Aspect-Based Sentiment Analysis (ABSA) requires models to identify sentiment toward specific aspects. Traditional counterfactual generation methods often alter non-target aspects or cause semantic drift. This paper proposes CAVE-ABSA, a framework that localizes opinion spans, performs controlled rewriting, refines via repair module, and validates through aspect-level checks, semantic similarity, AMR structural preservation, edit minimality, fluency, and contradiction detection. It generates and validates aspect-level counterfactuals for robustness evaluation and data augmentation, ensuring models rely on aspect-grounded sentiment reasoning.

## Key points

- CAVE-ABSA achieves controlled rewriting by localizing opinion spans of target aspects, avoiding impact on non-target aspects.
- The framework includes a repair module and seven validation mechanisms to ensure semantic consistency and factual correctness of counterfactuals.
- Experiments show CAVE-ABSA generates high-quality aspect-level counterfactuals for robustness testing and data augmentation.
- This work provides a reliable method to test whether ABSA models truly rely on aspect-grounded sentiment reasoning.

## Full text

Aspect-Based Sentiment Analysis (ABSA) requires models to identify sentiment toward specific aspects rather than relying on the global polarity of a sentence. This makes counterfactual evaluation especially challenging: a valid counterfactual should flip the sentiment of one target aspect while preserving the sentiment of all non-target aspects, semantic meaning, fluency, and factual consistency. Existing counterfactual generation methods often focus on sentence-level label flipping and may produce edits that are fluent but aspect-invalid, semantically drifting, or contradictory. To address this limitation, we propose CAVE-ABSA, a Constraint-Aware Validated Editing framework for generating and validating aspect-level counterfactuals. CAVE-ABSA localizes the opinion span associated with the target aspect, performs controlled counterfactual rewriting, refines candidates through a repair module, and filters them using aspect-level verification, semantic similarity, AMR-guided structural preservation, edit minimality, fluency, and contradiction detection. The framework is designed to construct validated counterfactual ABSA datasets for robustness evaluation and data augmentation. By explicitly separating generation from validation, CAVE-ABSA provides a principled approach for producing meaningful aspect-local counterfactuals and for testing whether ABSA models truly rely on aspect-grounded sentiment reasoning.

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