Challenge: Aspect Sentiment Quad Prediction (ASQP) is an extractive task that focuses on predicting tuples of sentiment-related elements from a given text.
Approach: They propose a stepwise syntax integration tuning framework that integrates syntactic structure knowledge into LLMs through a multi-step tuning process.
Outcome: The proposed framework integrates syntactic structure knowledge into large language models . it decomposes the quadruple generation task into two stages . the proposed framework significantly improves state-of-the-art performance across multiple datasets .

Similar Papers

Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction (2025.findings-emnlp)

Copied to clipboard

Challenge: Large language models lack task-specific alignment with ASQP . supervised small language models (SLMs) lack the extensive knowledge of LLMs.
Approach: They propose a framework that combines large language models and small language models to align LLM outputs with human preferences.
Outcome: The proposed framework improves Aspect Sentiment Quad Prediction performance by combining SLMs and LLMs.
An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions (2024.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for aspect-based sentiment analysis have not explored how to effectively leverage the knowledge of pre-trained language models to handle implicit aspects and opinions.
Approach: They propose a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad prediction for implicit aspects and opinions.
Outcome: The proposed framework significantly outperforms existing methods on benchmark datasets.
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)

Copied to clipboard

Challenge: Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure.
Approach: They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously.
Outcome: The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously.
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)

Copied to clipboard

Challenge: Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences.
Approach: They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction.
Outcome: The proposed method outperforms state-of-the-art methods significantly in low-resource settings.
Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)

Copied to clipboard

Challenge: Existing studies focus on predicting the four elements in one shot, instead of predicting them all.
Approach: They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence.
Outcome: The proposed method can generate the semantics of the sentiment elements in the natural language form.
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)

Copied to clipboard

Challenge: Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms.
Approach: They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions.
Outcome: The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets.
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)

Copied to clipboard

Challenge: Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns.
Approach: They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP.
Outcome: Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines.
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024.findings-acl)

Copied to clipboard

Challenge: Recent studies have developed powerful generative methods for aspect sentiment quad prediction (ASQP) but they still suffer from imprecise predictions and limited interpretability due to data scarcity and inadequate modeling of the quadruplet composition process.
Approach: They propose a self-consistent reasoning-based aspect sentiment quadruple prediction framework which generates reasonings and corresponding quadruples in sequence.
Outcome: The proposed model significantly improves its ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in aspect sentiment quadr uplp prediction.
Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025.findings-naacl)

Copied to clipboard

Challenge: Existing models confuse implicit and explicit sentiment, making it difficult to extract quadruples effectively.
Approach: They propose a framework that leverages distinct labeled features from diverse reviews and incorporates pseudo-token prompts to harness the semantic knowledge of pre-trained models.
Outcome: The proposed framework improves over four public datasets, averaging 1.99% F1 improvement, particularly in instances involving implicit sentiment.
Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain (2025.coling-main)

Copied to clipboard

Challenge: Syntactic structures are crucial for capturing aspect-opinion relationships . syntactically based models struggle with linguistic complexities .
Approach: They propose a syntactic-opinion-sentiment reasoning framework that leverages syntaktic information to improve ABSA performance.
Outcome: The proposed framework improves ABSA performance, though smaller LLMs exhibit weaker performance.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations