Cross-Domain Review Generation for Aspect-Based Sentiment Analysis
Jianfei Yu, Chenggong Gong, Rui Xia · 2021
Supervised learning methods have proven to be effective for Aspect-Based Sentiment Analysis (ABSA).However, the lack of finegrained labeled data hinders their effectiveness in many domains.To address this issue, unsupervised domain adaptation methods are desired to transfer knowledge from a labeled source domain to any unlabeled target domain.In this paper, we propose a new domain adaptation paradigm called cross-domain review generation (CDRG), which aims to generate target-domain reviews with fine-grained annotation based on the source-domain labeled reviews.To achieve this goal, we propose a two-step approach as a concrete realization of CDRG.It first converts a sourcedomain review to a domain-independent review by masking its source-specific attributes, and then converts the domain-independent review to a target-domain review with a masked language model pre-trained in the target domain.We further propose two ways to leverage the generated target-domain reviews for two cross-domain ABSA tasks.Extensive experiments demonstrate the superiority of our CDRG-based approaches over the state-of-theart domain adaptation methods.