# PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction arXiv: https://arxiv.org/abs/2110.04794 ยท HTML: https://mlsanity.com/paper/2110.04794/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 31 references that are in the map. - First posted on arXiv 10 Oct 2021. ## Builds on (most cited first, 21 of 21) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019) - [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019) - [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020) - [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019) - [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018) - [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) - [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) - [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) - [A Multi-task Learning Framework for Opinion Triplet Extraction](https://mlsanity.com/paper/2010.01512/) (2020) - [Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1712.05403/) (2017) - [DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.13816/) (2020) - [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021) - [Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach](https://mlsanity.com/paper/2105.03983/) (2021) ## Built on by (most cited first, 18 of 18) - [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022) - [DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2211.05705/) (2022) - [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022) - [A Unified One-Step Solution for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2306.04152/) (2023) - [A Generative Approach for Financial Causality Extraction](https://mlsanity.com/paper/2204.05674/) (2022) - [A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2306.10042/) (2023) - [CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2310.15577/) (2023) - [Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2305.17448/) (2023) - [A semantically enhanced dual encoder for aspect sentiment triplet extraction](https://mlsanity.com/paper/2306.08373/) (2023) - [ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach](https://mlsanity.com/paper/2205.00440/) (2022) - [A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2212.09052/) (2022) - [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023) - [A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data](https://mlsanity.com/paper/2505.09286/) (2025) - [PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture](https://mlsanity.com/paper/2211.12157/) (2022) - [Opinion Tree Parsing for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2306.08925/) (2023) - [An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2311.01713/) (2023) - [Aspect and Opinion Term Extraction Using Graph Attention Network](https://mlsanity.com/paper/2404.19260/) (2024) - [DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2606.01323/) (2026) ## Abstract (arXiv) Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end. Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment. Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in the recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence. We report our results both with and without BERT and also demonstrate the utility of domain-specific BERT post-training for the task.