Papers with ASQP
Generative Data Augmentation for Aspect Sentiment Quad Prediction (2023.starsem-1)
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| Challenge: | Existing approaches to analyze text contain rewrites and inconsistency between text and quads. |
| Approach: | They propose a new approach to analyze aspect terms, opinion terms, sentiment polarity in text . they augment quads and train a quads-to-text model to generate corresponding texts . |
| Outcome: | The proposed method outperforms existing methods and achieves state-of-the-art performance on two datasets. |
Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis (2025.acl-short)
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| Challenge: | Aspect-based sentiment analysis (ABSA) assesses sentiments towards aspects within texts, resulting in detailed sentiment tuples. |
| Approach: | They propose a dynamic order template method that dynamically creates an order template that contains only the necessary views for each instance. |
| Outcome: | The proposed method improves F1 scores on ASQP and ACOS datasets while significantly reducing inference time. |
Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction (2025.findings-emnlp)
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| 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. |
Sentimental Image Generation for Aspect-based Sentiment Analysis (2025.findings-acl)
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| Challenge: | Recent work on textual Aspect-Based Sentiment Analysis (ABSA) has demonstrated promising performance, but limited semantics derived from raw data. |
| Approach: | They propose a method that provides visual semantics to reinforce textual ABSA by adding additional augmentations to the input data. |
| Outcome: | The proposed method can provide visual semantics to reinforce the textual extraction. |
Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025.findings-naacl)
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| 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. |
Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024.findings-acl)
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| Challenge: | Current studies on aspect-based sentiment analysis focus on essential content for model generation, ignoring the incorporation of various noise during training. |
| Approach: | They propose a grid noise-diffusion pinpoint network (GDP) model that incorporates three new modules to tackle generation instability. |
| Outcome: | The proposed model reduces the generation instability of model learning and outputs by incorporating Consistency Likelihood Learning and GDP-FOR. |
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements. |
| Approach: | They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements . |
| Outcome: | The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment. |
đť’®2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction (2025.findings-naacl)
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| 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 . |
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024.findings-acl)
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| 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. |
An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions (2024.findings-emnlp)
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| 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. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)
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| 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. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | generative methods have shown promising results for extracting sentiment quadruplets . compound sentences can contain multiple quadroutlets, making extraction difficult . |
| Approach: | They propose an Aspect Term Oriented Sentence Splitter which simplifies compound sentences into simpler and clearer forms. |
| Outcome: | The proposed method outperforms existing methods in ASQP and ACOS tasks. |
Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)
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| 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. |
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
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| 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. |
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)
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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah
| 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. |