Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis

TL;DR

This paper introduces a soft prompt-guided joint learning approach that leverages external linguistic features to improve cross-domain aspect term extraction, addressing the limitations of traditional transfer methods.

Contribution

It proposes a novel soft prompt-based method that learns domain-invariant representations and uses transferable soft prompts for better cross-domain aspect term extraction.

Findings

Effective in cross-domain scenarios

Outperforms existing methods on benchmark datasets

Demonstrates robustness with varied domain distributions

Abstract

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.