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

2026-05-29

AI Summary

Existing aspect sentiment triplet extraction (ASTE) systems lack fine-grained validation mechanisms, often yielding predictions that appear locally plausible but are globally invalid. To address this limitation, this work proposes FiVeD, a novel framework that introduces, for the first time, an adjustable fine-grained verification mechanism. FiVeD jointly optimizes multiple objectives through multi-task learning, including validity classification, quality scoring, error type identification, and diagnostic rationale generation. Leveraging large language models, the framework constructs pseudo-labels enriched with quality scores and justifications, enabling effective filtering or re-ranking of candidate triplets. Evaluated across multiple ASTE baseline models, FiVeD achieves an average F1 improvement of 3.53 points, substantially enhancing both extraction accuracy and system robustness.

Abstract

Aspect Sentiment Triplet Extraction (ASTE) aims to identify aspect terms, opinion terms, and sentiment polarities as structured triplets, providing essential inputs for downstream information system applications such as opinion mining, explainable recommendations, and review summarization. Prior work mainly focuses on end-to-end extraction, while post hoc verification of extracted triplets remains comparatively underexplored. This gap limits the reliability of ASTE systems, since predicted triplets may be locally plausible while being globally invalid. Moreover, candidate invalidity is multi-faceted and candidate usability is inherently graded, motivating a fine-grained verification mechanism that can filter or re-rank outputs from diverse extractors. In this paper, we propose FiVeD, a framework for Fine-grained Verification with Diagnostic reasoning supervision. Specifically, the verifier is trained with multiple complementary objectives, including validity classification and quality score estimation as primary tasks, with error type classification and rationale generation as auxiliary tasks. We define hierarchical error categories and construct plausible incorrect triplets under semantic and syntactic constraints, and leverage an off-the-shelf LLM with task-specific rubrics to produce quality scores and diagnostic rationales. During inference, the resulting quality scores are used to filter candidate outputs, supporting adjustable precision-recall tradeoffs. Experiments across multiple ASTE baselines demonstrate that FiVeD consistently improves extraction performance by up to 3.53 F1 points as a plug-and-play verification module.

Problem

• Aspect Sentiment Triplet Extraction
• Fine-grained Verification
• Post hoc Verification
• Triplet Validity
• Diagnostic Reasoning

Innovation

• Fine-grained Verification
• Diagnostic Reasoning
• Aspect Sentiment Triplet Extraction
• Quality Scoring
• Plug-and-play Module