Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024)

Abstract: Aspect-based Sentiment Analysis (ABSA) is extensively researched in the NLP community, yet related models face challenges due to data sparsity when shifting to a new domain. Hence, data augmentation for cross-domain ABSA has attracted increasing attention in recent years. However, two key points have been neglected in prior studies: First, target domain unlabeled data are labeled with pseudo labels by the model trained in the source domain with little quality control, leading to inaccuracy and error propagation. Second, the label and text patterns of generated labeled data are monotonous, thus limiting the robustness and generalization ability of trained ABSA models. In this paper, we aim to design a simple yet effective framework to address the above shortages in ABSA data augmentation, called Refining and Synthesis Data Augmentation (RSDA). Our framework roughly includes two steps: First, it refines generated labeled data using a natural language inference (NLI) filter to control data quality. Second, it synthesizes diverse labeled data via novel label composition and paraphrase approaches. We conduct experiments on 4 kinds of ABSA subtasks, and our framework outperforms 7 strong baselines, demonstrating its effectiveness.

## introduction aspect-based sentiment analysis (absa) is a fundamental sentiment analysis task that aims to analyze sentiments at the aspect level @xcite . it usually involves extracting several sentiment elements, including aspects, opinions, and sentiment polarities. for example, given a sentence: "it is the best sushi i ever had", the aspect term is "sushi", the corresponding opinion term is "best" and the sentiment polarity is "positive". absa has attracted more and more attention in the past decade (nguyen and shirai, ## conclusion in this paper, we propose a two-step data augmentation framework for cross-domain absa tasks. the first step controls sample quality and filters low-quality pseudo labels using the nli filter. the second step enhances the diversity of augmented data using label composition and paraphrase methods. we conduct 32 experiments in cross-domain settings to demonstrate the effectiveness of our framework, which outperforms 7 strong baselines. 4 our approach not only mitigates error propagation caused by incorrect pseudo-labels but also enhances the diversity and fluency of the generated labeled data in the target domain. it is simple yet effective to implement and extend to other domains and tasks without much effort. in the future, we will explore the generalization ability of our framework to other structural information extraction tasks. wenxuan zhang, xin li, yang deng, lidong bing, and wai lam. 2021b. towards generative aspect-based sentiment analysis. in proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pages 504-510.