Interventional Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2104.11681v1
• Date: Tue, 20 Apr 2021 07:54:29 GMT
• Title: Interventional Aspect-Based Sentiment Analysis
• Authors: Zhen Bi, Ningyu Zhang, Ganqiang Ye, Haiyang Yu, Xi Chen, Huajun Chen
• Abstract summary: We propose a simple yet effective method, namely, Sentiment Adjustment (SENTA), by applying a backdoor adjustment to disentangle those confounding factors. Experimental results on the Aspect Robustness Test Set (ARTS) dataset demonstrate that our approach improves the performance while maintaining accuracy in the original test set.
• Score: 10.974711813144554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects. In this paper, we take a causal view to addressing this issue. We propose a simple yet effective method, namely, Sentiment Adjustment (SENTA), by applying a backdoor adjustment to disentangle those confounding factors. Experimental results on the Aspect Robustness Test Set (ARTS) dataset demonstrate that our approach improves the performance while maintaining accuracy in the original test set.

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