Dependency Reasoning for Aspect Sentiment Quadruple Prediction
Aspect Sentiment Quadruple Prediction (ASQP) is fundamentally a relational reasoning problem: identifying aspect-opinion-category-sentiment quadruples requires modeling structured dependencies, not merely sequential co-occurrence.
This paper introduces DRIFT, a method for Aspect Sentiment Quadruple Prediction (ASQP) that leverages dependency parsing to improve relational reasoning in natural language processing tasks.
What happened
The article presents DRIFT, a framework that addresses the relational reasoning challenges in ASQP by directly incorporating syntactic dependency structures into large language model prompts, improving performance through explicit relational context and syntax-aware evaluation mechanisms.
Why it matters
Improved ASQP methods enhance sentiment analysis accuracy, which is critical for applications like product review analysis and customer feedback systems where understanding aspect-opinion relationships is essential.
- Dependency parsing provides critical relational context for ASQP tasks
- Explicit syntactic information improves model performance beyond sequential patterns
- Syntax-aware evaluation mechanisms reveal hidden model capabilities
The bigger picture
The method demonstrates that explicit syntactic context injection can significantly improve structured prediction tasks in NLP, particularly for scenarios with varying aspect-opinion distances.
DRIFT advances ASQP by directly modeling aspect-opinion dependencies through dependency parse trees, achieving state-of-the-art results while highlighting the importance of syntactic context in relational reasoning tasks.
Technical details
- Dependency parse tree linearization
- Retrieval-augmented in-context learning
- Syntax-aware LLM-based adjudication
- Span-boundary variation handling
FAQ
What is the main innovation of DRIFT?
DRIFT's key innovation is linearizing dependency parse trees and injecting them directly into LLM prompts as explicit relational context, enabling more accurate aspect-opinion relationship modeling.
How does DRIFT improve upon existing methods?
Unlike previous approaches that use syntactic information indirectly, DRIFT incorporates dependency structures directly during generation, while also using retrieval-augmented learning and syntax-aware evaluation to handle span boundary variations.
What datasets were used to evaluate DRIFT?
DRIFT was tested on SemEval Rest15, Rest16, and ACOS datasets, achieving 54.47% and 64.10% F1 scores on Rest15 and Rest16 respectively.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.CL (NLP) on Oct 14, 2026.
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