Abstract
As a fundamental task in opinion mining, aspect and opinion co-extraction aims to identify the aspect terms and opinion terms in reviews. However, due to the lack of fine-grained annotated resources, it is hard to train a robust model for many domains. To alleviate this issue, unsupervised domain adaptation is proposed to transfer knowledge from a labeled source domain to an unlabeled target domain. In this paper, we propose a new Generative Cross-Domain Data Augmentation framework for unsupervised domain adaptation. The proposed framework is aimed to generate target-domain data with fine-grained annotation by exploiting the labeled data in the source domain. Specifically, we remove the domain-specific segments in a source-domain labeled sentence, and then use this as input to a pre-trained sequence-to-sequence model BART to simultaneously generate a target-domain sentence and predict the corresponding label for each word. Experimental results on three datasets demonstrate that our approach is more effective than previous domain adaptation methods.- Anthology ID:
- 2022.naacl-main.312
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4219–4229
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.312
- DOI:
- 10.18653/v1/2022.naacl-main.312
- Cite (ACL):
- Junjie Li, Jianfei Yu, and Rui Xia. 2022. Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4219–4229, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (Li et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.naacl-main.312.pdf
- Code
- nustm/gcdda