DINER : Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference (2024)

Abstract: Though notable progress has been made, neural-based aspect-based sentiment analysis (ABSA) models are prone to learn spurious correlations from annotation biases, resulting in poor robustness on adversarial data transformations. Among the debiasing solutions, causal inference-based methods have attracted much research attention, which can be mainly categorized into causal intervention methods and counterfactual reasoning methods. However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review). In this paper, we propose a novel framework based on multi-variable causal inference for debiasing ABSA. In this framework, different types of biases are tackled based on different causal intervention methods. For the review branch, the bias is modeled as indirect confounding from context, where backdoor adjustment intervention is employed for debiasing. For the aspect branch, the bias is described as a direct correlation with labels, where counterfactual reasoning is adopted for debiasing. Extensive experiments demonstrate the effectiveness of the proposed method compared to various baselines on the two widely used real-world aspect robustness test set datasets.

## introduction aspect-based sentiment analysis (absa) aims to classify the polarity of the sentiment (e.g., positive, negative, or neutral) towards a specific aspect of a sentence (e.g., bugers in the review "tasty bugers, and crispy fries.") @xcite @xcite @xcite . most absa methods solve the task as an input-output mapping problem based on high-capacity neural networks and pre-trained language models @xcite @xcite . though remarkable progress has been made, it is demonstrated that these state-of-the-art models are not robust in data transformation where simply reversing the polarity of the target results in over 20% drop in accuracy @xcite . a reasonable explanation is that neural networks trained with the stochastic gradient descent algorithm are vulnerable to annotation biases and learn the shortcuts instead of the underlying task @xcite . as shown in figure 1 (a), over 50.0% of targets have only one kind of polarity label in the widely used semeval 2014 laptop and restaurant datasets @xcite . for 83.9% and 79.6% instances in the test sets, the sentiments of the target aspect and all non-target aspects are the same. therefore, it is easy for end-to-end neural models to learn such spurious correlations and make predictions solely based on target aspects or sentiment words describing non-target aspects. to avoid learning spurious correlations, recent methods focus on debiasing, which can be categorized into argumentation-based methods @xcite , reweight trainingbased methods @xcite and causal inference-based methods @xcite . among them, causal inference attracts much research interest for its theoretical-granted property and little modification to the existing learning paradigm. @xcite proposed a debiasing method for the language bias in the vision question answering task by performing counterfactual reasoning. @xcite employed backdoor adjustment-based intervention for mitigating the context bias in object detection. recent attempts have been made to solve various biases in natural language processing tasks, including natural language understanding @xcite , implicit sentiment analysis @xcite , and fact verification @xcite . however, most causal inference-based debiasing methods are based on single-variable causal inference, which is not appropriate for absa with two input variables. as shown in figure 1 (a), there are two types of biases in absa. the target aspects a are often directly correlated with the polarity labels l, while the sentiment words for targets in the review r are often indirectly confounded with the non-targets c. to further investigate the difference between aspect-related biases and review-related biases, a simple experiment is conducted by training two probing models with only review or aspect as input. as shown in figure 1 (b) , the aspect-only model has similar performances on the original and the adversarial test set except revtgt where spurious correlations learned in the training set are flipped, while the review-only model performs differently on four test variants. it might suggest that the biases in the aspect branch are direct and simple, while the biases in the review branch are indirect and complicated, which poses a challenge. ## methods in this section, we will introduce the proposed method, diner, in detail. first, we will define the structural causal model (scm) of absa and derive the formula of causal effect step by step. then, we will formulate how to estimate the components in the causal effect formula with backdoor adjustment and counterfactual reasoning. finally, we will introduce the training and inference processes. ## result and analysis we evaluate diner in two backbones: bertbased and roberta-based. these configurations are set to evaluate the effectiveness of diner when integrated with different encoder-only plms. and diner based on roberta tends to outperform its bert counterparts, which may be attributed to roberta's more robust pre-training on a larger and more diverse corpus, leading to better generalization capabilities @xcite . the results are compelling, showing that diner(roberta-based) model achieves the state-of-the-art performance across all metrics in both the laptop and restaurant domains, with a notable acc. of 76.51 and 82.46, f1-scores of 73.27 and 76.92, and ars of 59.40 and 64.02, respectively. diner(roberta-based) demonstrates superior performance in the laptop domain, outpacing the baseline roberta † by margins of 1.55, 1.11, and 3.13 in terms of acc., f1-score, and ars metrics, respectively. in the restaurant domain, the model further extends its lead, achieving improvements of 3.20, 6.45, and 4.06 in the same metrics. similarly, the diner(bert-based) exhibits empirical enhancements. ## conclusion in this paper, to debias the target and review in absa simultaneously, a novel debiasing framework, diner, is proposed with multi-variable causal inference. specifically, the aspect is assumed to have a direct correlation with the label, so a counterfactual reasoning-based intervention is employed to debias the aspect branch. in the meantime, the sentiment words towards the target in the review are assumed to be indirectly confounded with the context, where a backdoor adjustmentbased intervention is employed to debias the review branch. extensive experiments show the effectiveness of the proposed method in debiasing absa compared to normal state-of-the-art absa methods and debiasing methods.