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.

本研究提出了一种基于句法导向域自适应模型的新颖跨领域 ABSA 方法，该方法利用句法结构相似性构建伪训练实例，同时引入一种基于句法的 BERT 掩码语言模型以进一步捕获领域不变特征；最后，为了减轻跨领域 End2End ABSA 中的情感不一致问题，引入了基于跨度的联合方面和情感模块。在五个基准数据集上的实验结果表明，与现有模型相比，我们的模型在跨领域 End2End ABSA 任务的微 F1 指标方面始终表现出色。