Aspect term extraction is a fundamental task in fine-grained sentiment analysis, which aims at detecting customer's opinion targets from reviews on product or service. The traditional supervised models can achieve promising results with annotated datasets, however, the performance dramatically decreases when they are applied to the task of cross-domain aspect term extraction. Existing cross-domain transfer learning methods either directly inject linguistic features into Language models, making it difficult to transfer linguistic knowledge to target domain, or rely on the fixed predefined prompts, which is time-consuming to construct the prompts over all potential aspect term spans. To resolve the limitations, we propose a soft prompt-based joint learning method for cross domain aspect term extraction in this paper. Specifically, by incorporating external linguistic features, the proposed method learn domain-invariant representations between source and target domains via multiple objectives, which bridges the gap between domains with varied distributions of aspect terms. Further, the proposed method interpolates a set of transferable soft prompts consisted of multiple learnable vectors that are beneficial to detect aspect terms in target domain. Extensive experiments are conducted on the benchmark datasets and the experimental results demonstrate the effectiveness of the proposed method for cross-domain aspect terms extraction.

翻译：方面术语提取是细粒度情感分析中的基础任务，旨在从产品或服务评论中检测客户的意见目标。传统监督模型在标注数据集上能取得良好效果，但应用于跨域方面术语提取任务时性能显著下降。现有跨域迁移学习方法要么直接将语言特征注入语言模型，导致难以将语言知识迁移至目标域；要么依赖固定的预定义提示，需耗费大量时间构建覆盖所有潜在方面术语跨度的提示词。为解决这些局限，本文提出一种基于软提示的跨域方面术语提取联合学习方法。具体而言，该方法通过融入外部语言特征，利用多目标学习获取源域与目标域之间的域不变表示，从而弥合具有不同方面术语分布特征的两个域之间的差距。此外，该方法引入一组由多个可学习向量构成的可迁移软提示，这些软提示有助于检测目标域中的方面术语。在基准数据集上进行的大量实验表明，所提方法在跨域方面术语提取任务中具有有效性。