Grunnleggende konsepter
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.
Sammendrag
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.
Statistikk
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.