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

Huaishao Luo, Lei Ji, Tianrui Li, Daxin Jiang, Nan Duan

Abstract
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.
Anthology ID:
2020.findings-emnlp.6
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–64
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.6
DOI:
10.18653/v1/2020.findings-emnlp.6
Bibkey:
Cite (ACL):
Huaishao Luo, Lei Ji, Tianrui Li, Daxin Jiang, and Nan Duan. 2020. GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 54–64, Online. Association for Computational Linguistics.
Cite (Informal):
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (Luo et al., Findings 2020)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2020.findings-emnlp.6.pdf
Code
 ArrowLuo/GRACE
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