# Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation

arXiv: https://arxiv.org/abs/2210.10291 · HTML: https://mlsanity.com/paper/2210.10291/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2203.09101/) (2022)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/1812.10735/) (2018)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)

## Built on by (most cited first, 30 of 30)

- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy](https://mlsanity.com/paper/2403.00354/) (2024)
- [Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.00418/) (2023)
- [Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2502.13044/) (2025)
- [MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.16956/) (2023)
- [BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.07365/) (2024)
- [Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.18078/) (2024)
- [LLaMA-Based Models for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2508.08649/) (2025)
- [Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models](https://mlsanity.com/paper/2508.10366/) (2025)
- [PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2412.00763/) (2024)
- [STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2501.16093/) (2025)
- [Adaptive Data Augmentation for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2401.06394/) (2024)
- [E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2405.06454/) (2024)
- [ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA](https://mlsanity.com/paper/2405.20274/) (2024)
- [Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2406.11130/) (2024)
- [Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation](https://mlsanity.com/paper/2410.09807/) (2024)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding](https://mlsanity.com/paper/2508.10369/) (2025)
- [Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2511.23184/) (2025)
- [Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2603.13777/) (2026)
- [OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2309.13297/) (2023)
- [Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2404.11539/) (2024)
- [Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples](https://mlsanity.com/paper/2505.10389/) (2025)
- [LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2603.01778/) (2026)
- [nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models](https://mlsanity.com/paper/2603.01788/) (2026)
- [S2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2604.23296/) (2026)
- [Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2604.26619/) (2026)
- [Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2605.28058/) (2026)
- [STAR-OPD: Structured Aspect-Cascade-Aware On-Policy Reward Distillation for ABSA Quadruple Extraction](https://mlsanity.com/paper/2608.20831/) (2026)
- [Dependency Reasoning for Aspect Sentiment Quadruple Prediction](https://mlsanity.com/paper/2610.14873/) (2026)

## Abstract (arXiv)

Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis. Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect term, opinion term, sentiment polarity). The template involves the four elements in a fixed order. However, we observe that this solution contradicts with the order-free property of the ASQP task, since there is no need to fix the template order as long as the quadruplet is extracted correctly. Inspired by the observation, we study the effects of template orders and find that some orders help the generative model achieve better performance. It is hypothesized that different orders provide various views of the quadruplet. Therefore, we propose a simple but effective method to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the ASQP task. Specifically, we use the pre-trained language model to select the orders with minimal entropy. By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.