Abstract
Cross-domain sentiment classification aims to address the lack of massive amounts of labeled data. It demands to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. In this paper, we investigate how to efficiently apply the pre-training language model BERT on the unsupervised domain adaptation. Due to the pre-training task and corpus, BERT is task-agnostic, which lacks domain awareness and can not distinguish the characteristic of source and target domain when transferring knowledge. To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task. The post-training procedure will encourage BERT to be domain-aware and distill the domain-specific features in a self-supervised way. Based on this, we could then conduct the adversarial training to derive the enhanced domain-invariant features. Extensive experiments on Amazon dataset show that our model outperforms state-of-the-art methods by a large margin. The ablation study demonstrates that the remarkable improvement is not only from BERT but also from our method.- Anthology ID:
- 2020.acl-main.370
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4019–4028
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.370
- DOI:
- 10.18653/v1/2020.acl-main.370
- Cite (ACL):
- Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020. Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4019–4028, Online. Association for Computational Linguistics.
- Cite (Informal):
- Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis (Du et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.370.pdf