Abstract
Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corpora can have unforeseen impacts, including differing model performance for different user groups, as well as privacy implications. In this paper, we propose an approach to explicitly obscure important author characteristics at training time, such that representations learned are invariant to these attributes. Evaluating on two tasks, we show that this leads to increased privacy in the learned representations, as well as more robust models to varying evaluation conditions, including out-of-domain corpora.- Anthology ID:
- P18-2005
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–30
- Language:
- URL:
- https://aclanthology.org/P18-2005
- DOI:
- 10.18653/v1/P18-2005
- Cite (ACL):
- Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018. Towards Robust and Privacy-preserving Text Representations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 25–30, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Towards Robust and Privacy-preserving Text Representations (Li et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P18-2005.pdf
- Code
- lrank/Robust_and_Privacy_preserving_Text_Representations + additional community code