Abstract
In this paper, we propose conditional adversarial networks (CANs), a framework that explores the relationship between the shared features and the label predictions to impose stronger discriminability to the learned features, for multi-domain text classification (MDTC). The proposed CAN introduces a conditional domain discriminator to model the domain variance in both the shared feature representations and the class-aware information simultaneously, and adopts entropy conditioning to guarantee the transferability of the shared features. We provide theoretical analysis for the CAN framework, showing that CAN’s objective is equivalent to minimizing the total divergence among multiple joint distributions of shared features and label predictions. Therefore, CAN is a theoretically sound adversarial network that discriminates over multiple distributions. Evaluation results on two MDTC benchmarks show that CAN outperforms prior methods. Further experiments demonstrate that CAN has a good ability to generalize learned knowledge to unseen domains.- Anthology ID:
- 2021.adaptnlp-1.3
- Volume:
- Proceedings of the Second Workshop on Domain Adaptation for NLP
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine
- Editors:
- Eyal Ben-David, Shay Cohen, Ryan McDonald, Barbara Plank, Roi Reichart, Guy Rotman, Yftah Ziser
- Venue:
- AdaptNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–27
- Language:
- URL:
- https://aclanthology.org/2021.adaptnlp-1.3
- DOI:
- Cite (ACL):
- Yuan Wu, Diana Inkpen, and Ahmed El-Roby. 2021. Conditional Adversarial Networks for Multi-Domain Text Classification. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 16–27, Kyiv, Ukraine. Association for Computational Linguistics.
- Cite (Informal):
- Conditional Adversarial Networks for Multi-Domain Text Classification (Wu et al., AdaptNLP 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.adaptnlp-1.3.pdf
- Data
- Multi-Domain Sentiment