# Aspect Sentiment Triplet Extraction Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.06107 · HTML: https://mlsanity.com/paper/2108.06107/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [A Unified Model for Opinion Target Extraction and Target Sentiment Prediction](https://mlsanity.com/paper/1811.05082/) (2018)
- [A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://mlsanity.com/paper/1811.03925/) (2018)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [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)
- [First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2102.08549/) (2021)
- [Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment](https://mlsanity.com/paper/2010.01357/) (2020)

## Built on by (most cited first, 12 of 12)

- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach](https://mlsanity.com/paper/2211.10018/) (2022)
- [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022)
- [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021)
- [MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2406.11234/) (2024)
- [A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2306.10042/) (2023)
- [A semantically enhanced dual encoder for aspect sentiment triplet extraction](https://mlsanity.com/paper/2306.08373/) (2023)
- [A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2212.09052/) (2022)
- [Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information](https://mlsanity.com/paper/2302.10195/) (2023)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2208.09617/) (2022)
- [Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2604.09123/) (2026)

## Abstract (arXiv)

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expressed sentiments. Previous approaches to ASTE usually simultaneously extract all three components or first identify the aspect and opinion terms, then pair them up to predict their sentiment polarities. In this work, we present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment in a hierarchical reinforcement learning (RL) framework. We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment. This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency. Furthermore, this hierarchical RLsetup enables us to deal with multiple and overlapping triplets. In our experiments, we evaluate our model on existing datasets from laptop and restaurant domains and show that it achieves state-of-the-art performance. The implementation of this work is publicly available at https://github.com/declare-lab/ASTE-RL.