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.

本研究提出了一种基于软提示的联合学习方法，通过结合外部语言特征，实现了源域和目标域之间的不变表示，通过多个目标桥接了不同分布的方面术语，插值了一组可转移的软提示来检测目标域中的方面术语。