# GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/2009.10557 · HTML: https://mlsanity.com/paper/2009.10557/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

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

- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2302.08624/) (2023)
- [Open Aspect Target Sentiment Classification with Natural Language Prompts](https://mlsanity.com/paper/2109.03685/) (2021)
- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization](https://mlsanity.com/paper/2502.15821/) (2025)
- [Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.03806/) (2021)
- [Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.18035/) (2024)
- [Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews](https://mlsanity.com/paper/2408.13202/) (2024)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [EmoGRACE: Aspect-based emotion analysis for social media data](https://mlsanity.com/paper/2503.15133/) (2025)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)

## Abstract (arXiv)

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities. We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. The polarities sequence is designed to depend on the generated aspect terms labels. To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone. Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.