Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis

• URL: http://arxiv.org/abs/2211.05457v2
• Date: Wed, 16 Aug 2023 02:58:59 GMT
• Title: Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis
• Authors: Anguo Dong, Cuiyun Gao, Yan Jia, Qing Liao, Xuan Wang, Lei Wang, and Jing Xiao
• Abstract summary: Domain adaptation is a popular solution to alleviate the data deficiency issue in new domains by transferring common knowledge across domains. We propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA. Our model consistently outperforms the state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain End2End ABSA task.
• Score: 23.883810236153757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect terms in review texts and determining their sentiment polarities, which is widely studied in both academia and industry. As a fine-grained classification task, the annotation cost is extremely high. Domain adaptation is a popular solution to alleviate the data deficiency issue in new domains by transferring common knowledge across domains. Most cross-domain ABSA studies are based on structure correspondence learning (SCL), and use pivot features to construct auxiliary tasks for narrowing down the gap between domains. However, their pivot-based auxiliary tasks can only transfer knowledge of aspect terms but not sentiment, limiting the performance of existing models. In this work, we propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA. SDAM exploits syntactic structure similarities for building pseudo training instances, during which aspect terms of target domain are explicitly related to sentiment polarities. Besides, we propose a syntax-based BERT mask language model for further capturing domain-invariant features. Finally, to alleviate the sentiment inconsistency issue in multi-gram aspect terms, we introduce a span-based joint aspect term and sentiment analysis module into the cross-domain End2End ABSA. Experiments on five benchmark datasets show that our model consistently outperforms the state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain End2End ABSA task.

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