Mixture of Hybrid Prompts for Cross-Domain Aspect Sentiment Triplet Extraction

Abstract

Cross-domain Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets from the review of a target domain, utilizing knowledge from a source domain. As a newly proposed task, limited work has been devoted to it. Except for solving it in a zero-shot manner with in-domain models, recent work explores a bidirectional generative framework to generate pseudo-labeled target data. However, such a method suffers from low efficiency with two-stage training and unstable pseudo-label quality. In this paper, we propose a Hybrid Prompts Mixture (HiPM) method for cross-domain ASTE to fully utilize domain-independent knowledge. Within this method, given that syntax information is an essential linguistic feature for triplet extraction, we design a syntax-related hard prompt to transfer the structures. Additionally, aspects from different domains exhibit similarities in their respective categories. We take this shared information as the prototypes and enrich them through a warm-up step. The resulting prototypes then act as the source of soft prompts. We further mix the hard and soft prompts with the original sequence into a generative model to extract triplets. Experimental results show that our method outperforms baselines on twelve transfer pairs, and obtains a 1.48% average F1 score improvement over the state-of-the-art cross-domain ASTE model.