Multimodal aspect-based sentiment classification (MABSC) requires fine-grained reasoning over both textual and visual content to infer sentiments toward specific aspects.However, existing methods often rely on superficial correlations-particularly between aspect terms and sentiment labels-leading to poor generalization and vulnerability to spurious cues.To address this limitation, we propose DPCI, a novel Dual-Path Counterfactual Integration framework that enhances model robustness by explicitly modeling counterfactual reasoning in multimodal contexts.Specifically, we design a dual counterfactual generation module that simulates two types of interventions: replacing aspect terms and rewriting descriptive content, thereby disentangling the spurious dependencies from causal sentiment cues.We further introduce a sample-aware counterfactual selection strategy to retain high-quality, diverse counterfactuals tailored to each generation path.Finally, a confidence-guided integration mechanism adaptively fuses counterfactual signals into the main prediction stream.Extensive experiments on standard MABSC benchmarks demonstrate that DPCI not only achieves stateof-the-art performance but also significantly improves model robustness.

Multimodal aspect-based sentiment classification (MABSC) is a challenging task emerging in recent years, which aims to combine text and image to identify the sentiment polarity of each aspect. There exists a potential irrelevance between aspects and images, and mistakenly focusing on irrelevant image regions will introduce redundant and misalignment noise. Besides, existing methods implicitly mix visual and textual features, which may lead to the loss or blurring of modality-specific information. To address these challenges, we propose a Bottleneck-constrained Contrastive Decoupled Network (BCDN) for the MABSC task. We first design a bottleneck-constrained visual consistency module to reduce redundancy and misalignment noise in aspect-related visual features. Additionally, we employ modality decoupling to fully capture inter-modality knowledge. Specifically, we first decouple the aspect-related visual and textual representations into modality-invariant and modality-specific features. Afterwards, we propose novel contrastive regularizations to optimize the decoupled features. Extensive experiments on benchmark datasets demonstrate that our BCDN achieves superior performance and verify the effectiveness of our BCDN. The codes are released at https://github.com/ruiliu2020/BCDN.

Jiahao Cao, Rui Liu, Huailiang Peng, Lei Jiang, Xu Bai. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to identify the polarity of a target aspect. Some works introduce various attention mechanisms to fully mine the relevant context words of different aspects, and use the traditional cross-entropy loss to fine-tune the models for the ABSA task. However, the attention mechanism paying partial attention to aspect-unrelated words inevitably introduces irrelevant noise. Moreover, the cross-entropy loss lacks discriminative learning of features, which makes it difficult to exploit the implicit information of intra-class compactness and inter-class separability. To overcome these challenges, we propose an Aspect Feature Distillation and Enhancement Network (AFDEN) for the ABSA task. We first propose a dual-feature extraction module to extract aspect-related and aspect-unrelated features through the attention mechanisms and graph convolutional networks. Then, to eliminate the interference of aspect-unrelated words, we design a novel aspect-feature distillation module containing a gradient reverse layer that learns aspect-unrelated contextual features through adversarial training, and an aspect-specific orthogonal projection layer to further project aspect-related features into the orthogonal space of aspect-unrelated features. Finally, we propose an aspect-feature enhancement module that leverages supervised contrastive learning to capture the implicit information between the same sentiment labels and between different sentiment labels. Experimental results on three public datasets demonstrate that our AFDEN model achieves state-of-the-art performance and verify the effectiveness and robustness of our model.