# A Unified Generative Framework for Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2106.04300 · HTML: https://mlsanity.com/paper/2106.04300/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 30 of 31)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [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)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://mlsanity.com/paper/1805.01086/) (2018)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Aspect Term Extraction with History Attention and Selective Transformation](https://mlsanity.com/paper/1805.00760/) (2018)
- [Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1603.06679/) (2016)
- [Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation](https://mlsanity.com/paper/2004.14769/) (2020)
- [Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision](https://mlsanity.com/paper/1907.03750/) (2019)
- [Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2001.01989/) (2020)
- [Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning](https://mlsanity.com/paper/2010.13378/) (2020)

## Built on by (most cited first, 30 of 97)

- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [Intention Analysis Makes LLMs A Good Jailbreak Defender](https://mlsanity.com/paper/2401.06561/) (2024)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2109.08306/) (2021)
- [InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2302.08624/) (2023)
- [Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2204.07955/) (2022)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2210.06629/) (2022)
- [PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2408.09481/) (2024)
- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)
- [A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.07832/) (2022)
- [Mirror: A Universal Framework for Various Information Extraction Tasks](https://mlsanity.com/paper/2311.05419/) (2023)
- [Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2110.13398/) (2021)
- [Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.05496/) (2024)
- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning](https://mlsanity.com/paper/2211.10986/) (2022)
- [AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.01004/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)
- [UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective](https://mlsanity.com/paper/2305.10306/) (2023)
- [Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis](https://mlsanity.com/paper/2203.07090/) (2022)
- [Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation](https://mlsanity.com/paper/2211.01467/) (2022)

## Abstract (arXiv)

Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a unified framework. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. Based on the unified formulation, we exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework. Extensive experiments on four ABSA datasets for seven subtasks demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole ABSA subtasks, which could benefit multiple tasks.