Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing · 2023
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.Since labeled data only exists in the source domain, a model is expected to bridge the domain gap for tackling cross-domain ABSA.Though domain adaptation methods have proven to be effective, most of them are based on a discriminative model, which needs to be specifically designed for different ABSA tasks.To offer a more general solution, we propose a unified bidirectional generative framework to tackle various cross-domain ABSA tasks.Specifically, our framework trains a generative model in both text-to-label and label-to-text directions.The former transforms each task into a unified format to learn domain-agnostic features, and the latter generates natural sentences from noisy labels for data augmentation, with which a more accurate model can be trained.To investigate the effectiveness and generality of our framework, we conduct extensive experiments on four cross-domain ABSA tasks and present new state-of-the-art results on all tasks.Our data and code are publicly available at https://github.com