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 https://github.com/DAMO-NLP-SG/BGCA.- Anthology ID:
- 2023.acl-long.686
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12272–12285
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.686
- DOI:
- 10.18653/v1/2023.acl-long.686
- Cite (ACL):
- Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. 2023. Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12272–12285, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (Deng et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.acl-long.686.pdf