Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference

Core Concepts

The author proposes a novel framework, DINER, based on multi-variable causal inference to debias aspect-based sentiment analysis by addressing biases in both the target aspect and the review branch.

Abstract

The paper introduces DINER, a framework for debiasing aspect-based sentiment analysis using multi-variable causal inference. It addresses biases in both the target aspect and the review branch through different interventions. The proposed method shows effectiveness compared to existing baselines on real-world datasets. Key Points: Neural ABSA models prone to learning spurious correlations from annotation biases. Causal inference methods attract research interest for debiasing ABSA. DINER framework uses multi-variable causal inference for debiasing ABSA. Different interventions are employed for bias mitigation in the review and aspect branches. Extensive experiments demonstrate the effectiveness of DINER compared to baselines.

Stats

Among them, causal inference attracts much research interest for its theoretical-granted property and little modification to the existing learning paradigm. Recent attempts have been made to solve various biases in natural language processing tasks, including natural language understanding (Tian et al., 2022), implicit sentiment analysis (Wang et al., 2022), and fact verification (Xu et al., 2023). The proposed method shows effectiveness compared to various baselines on two widely used real-world aspect robustness test set datasets.

Quotes

Key Insights Distilled From

DINER

by Jialong Wu,L... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01166.pdf

Deeper Inquiries

How does the proposed DINER framework compare to other debiasing methods in terms of computational efficiency

The proposed DINER framework offers several advantages in terms of computational efficiency compared to other debiasing methods. One key aspect is the utilization of causal inference-based interventions, which allows for targeted and precise adjustments to mitigate biases in aspect-based sentiment analysis (ABSA). By focusing on multi-variable causal inference, DINER can effectively address biases related to both the target aspect and the review input simultaneously. This targeted approach reduces unnecessary computations and streamlines the debiasing process, leading to improved computational efficiency.

What implications could the findings of this study have on improving other natural language processing tasks

The findings of this study could have significant implications for improving various natural language processing tasks beyond ABSA. By introducing a novel framework like DINER that leverages multi-variable causal inference for debiasing, researchers can enhance the robustness and accuracy of models across different NLP applications. The insights gained from this study could be applied to tasks such as text classification, sentiment analysis, machine translation, and more. Additionally, understanding how biases impact model performance and implementing effective debiasing strategies can lead to more reliable NLP systems with better generalization capabilities.

How might incorporating additional contextual information impact the performance of the DINER framework

Incorporating additional contextual information into the DINER framework could potentially enhance its performance by providing a more comprehensive understanding of the input data. By expanding the scope of context features considered during debiasing interventions, DINER may capture nuanced relationships between aspects, sentiments, and contexts more effectively. This deeper contextual awareness could help improve model predictions by reducing spurious correlations and enhancing overall accuracy in ABSA tasks. Furthermore, incorporating richer contextual information may also contribute to better generalization on diverse datasets and real-world scenarios.