Fine-grained Verification via Diagnostic Reasoning Supervision for Aspect Sentiment Triplet Extraction

Researchers propose FiVeD, a fine-grained verification framework for Aspect Sentiment Triplet Extraction that improves extraction accuracy by up to 3.53 F1 points through multi-task learning with validity classification, quality scoring, error detection, and rationale generation. The framework addresses a critical gap in ASTE systems by post-hoc verification of extracted triplets, enabling adjustable precision-recall tradeoffs for downstream NLP applications.

This research addresses a fundamental limitation in sentiment analysis systems: while end-to-end extraction models have advanced considerably, the verification and quality assessment of their outputs remains underdeveloped. FiVeD fills this gap by introducing a verification layer that operates on top of existing extractors, treating it as a plug-and-play module rather than requiring complete system redesign. The framework's multi-task learning approach—combining validity classification, quality estimation, error categorization, and rationale generation—reflects a sophisticated understanding of how verification should work. By training on hierarchical error categories and synthetically generated invalid triplets, the verifier learns nuanced distinctions between plausible-but-incorrect and valid outputs.

The broader context shows growing recognition that NLP systems need both extraction and validation capabilities, particularly for downstream applications like recommendation engines and review systems where false positives carry real costs. The use of large language models to generate quality scores and diagnostic rationales introduces explainability, a critical requirement for enterprise adoption. This design allows practitioners to adjust filtering thresholds based on their specific use cases—high precision when errors are costly, higher recall when coverage matters more.

For the NLP industry, FiVeD demonstrates how verification mechanisms can become standardized post-processing steps, similar to ensemble methods in traditional ML. The consistent improvements across multiple baseline extractors suggest this approach has practical value across different architectural choices. The framework's reliance on LLM-based scoring also reflects industry trends toward leveraging foundation models for quality assessment tasks.

• →FiVeD improves ASTE performance by up to 3.53 F1 points through post-hoc verification rather than end-to-end redesign
• →Multi-task learning combining validity classification, quality scoring, error detection, and rationale generation enables fine-grained candidate assessment
• →The framework supports adjustable precision-recall tradeoffs, allowing practitioners to tune filtering thresholds for specific applications
• →Hierarchical error categorization and synthetically generated invalid triplets train the verifier to distinguish plausible-but-incorrect outputs
• →LLM-based quality scores and diagnostic rationales add explainability, supporting enterprise adoption in sentiment analysis systems