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.

Highlights

  • Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental, fine-grained subtasks in aspect-based sentiment analysis (ABSA)

  • Our GRadient hArmonized and CascadEd labeling model (GRACE) achieves state-of-the-art results over baselines

  • Comparing with the BASE, we believe the cascaded labeling strategy can make an interaction between aspect terms within a sentence, which enhances the judgment of sentiment labels

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Summary

Introduction

Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental, fine-grained subtasks in aspect-based sentiment analysis (ABSA). Collapsed O B-POS I-POS O B-POS (a) Label statistics (b) Gradient statistics. To better satisfy the practical applications, the aspect term-polarity co-extraction, which solves ATE and ASC simultaneously, receives much attention in recent years (Li et al, 2019b; Luo et al, 2019b; Hu et al, 2019; Wan et al, 2020). A big challenge of the aspect term-polarity co-extraction in a unified model is that ATE and ASC belong to different tasks: ATE is usually a sequence labeling task, and ASC is usually a classification task. Previous works usually transform the ASC task into sequence labeling.

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