# Aspect Sentiment Quad Prediction as Paraphrase Generation

arXiv: https://arxiv.org/abs/2110.00796 · HTML: https://mlsanity.com/paper/2110.00796/

## Key facts

- Cited by 100 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [A Unified Model for Opinion Target Extraction and Target Sentiment Prediction](https://mlsanity.com/paper/1811.05082/) (2018)
- [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/1906.06906/) (2019)
- [Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification](https://mlsanity.com/paper/1906.03820/) (2019)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Augmented Natural Language for Generative Sequence Labeling](https://mlsanity.com/paper/2009.13272/) (2020)
- [Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification](https://mlsanity.com/paper/1909.06276/) (2019)
- [A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02745/) (2016)
- [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)
- [DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction](https://mlsanity.com/paper/1906.01794/) (2019)
- [Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction](https://mlsanity.com/paper/1605.07843/) (2016)
- [First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2102.08549/) (2021)

## Built on by (most cited first, 30 of 100)

- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations](https://mlsanity.com/paper/2305.10172/) (2023)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation](https://mlsanity.com/paper/2210.10291/) (2022)
- [DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2211.05705/) (2022)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2210.06629/) (2022)
- [Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure](https://mlsanity.com/paper/2211.07743/) (2022)
- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2408.09481/) (2024)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (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)
- [UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning](https://mlsanity.com/paper/2211.10986/) (2022)
- [Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2502.13044/) (2025)
- [EFSA: Towards Event-Level Financial Sentiment Analysis](https://mlsanity.com/paper/2404.08681/) (2024)
- ["What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge](https://mlsanity.com/paper/2305.12091/) (2023)
- [The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment Quadruples: A Comparative Analysis](https://mlsanity.com/paper/2310.06502/) (2023)
- [Easy-to-Hard Learning for Information Extraction](https://mlsanity.com/paper/2305.09193/) (2023)
- [A Unified One-Step Solution for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.04152/) (2023)
- [Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing](https://mlsanity.com/paper/2306.06601/) (2023)
- [MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.16956/) (2023)
- [Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2309.15476/) (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)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel textscParaphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of cross-task transfer with the proposed unified textscParaphrase modeling framework.