Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis

• URL: http://arxiv.org/abs/2305.09509v1
• Date: Tue, 16 May 2023 15:02:23 GMT
• Title: Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
• Authors: Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing
• Abstract summary: 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. We propose a unified bidirectional generative framework to tackle various cross-domain ABSA tasks. Our framework trains a generative model in both text-to-label and label-to-text directions.
• Score: 68.742820522137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \url{https://github.com/DAMO-NLP-SG/BGCA}.

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