Source-linked AI summary
Towards Robust and Privacy-preserving Text Representations
Yitong Li, Timothy Baldwin, Trevor Cohn
TL;DR
Text reflects author attributes that can bias NLP performance across user groups and expose private information. The paper uses adversarial training to obscure demographic attributes, finding increased privacy while maintaining or improving task performance and robustness, including out-of-domain evaluation.
Problem
Author attributes in text can bias NLP models across user groups and leave sensitive information in transferred representations vulnerable to reverse engineering.
Method
The paper trains deep models with adversarial learning to explicitly obscure demographic attributes in hidden representations while supporting NLP inference.
Results
Evaluations on POS tagging and sentiment classification show increased privacy while maintaining or improving task performance, including in-domain and out-of-domain scenarios.
Takeaways & Limitations
Adversarially trained representations can reduce demographic information while improving robustness without affecting sentiment performance.
Takeaways & Limitations
The transfer evaluation lacks domain adaptation, and the authors expect performance to drop substantially across the differing corpora.
Abstract
from arXiv · showhide
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.
1 Introduction
Author-linked variation in text can bias NLP models across user groups and evaluation corpora, while text and learned representations can expose sensitive author information. The paper addresses both robustness and privacy by obscuring author attributes during training.
- Author, background, gender, age, education, and nationality influence language and can produce unequal model performance across user groups and corpora.
- Removing author-related confounds is expected to improve generalisation, especially for out-of-domain evaluation.
- Text can inadvertently reveal author information, creating privacy risks for emails, SMS messages, social media posts, and released search logs.
- Shared latent representations may still expose sensitive information and can be reverse engineered even when differential privacy mechanisms are applied.
- The proposed adversarial approach obscures gender, age, and location while supporting POS-tagging and sentiment analysis, with empirical privacy gains and especially stronger out-of-domain performance.
2 Methodology
The method learns task-useful representations while discouraging prediction of protected author attributes. Adversarial training reverses auxiliary-task gradients at the representation, and the objective extends to multiple protected attributes.
- The model maps inputs x to representations h, which parameterize a generalized linear predictor for target y trained with a differentiable supervised loss.
- Overfitting can make h capture training-instance or authorship specifics instead of generalizable sentiment-related semantics.
- Adversarial Learning: The architecture jointly trains a main predictor and discriminator so h supports target prediction but poorly predicts protected attributes such as gender.
- Adversarial Learning: Gradient reversal keeps auxiliary predictors effective while training h to be maximally good for the main task and maximally poor for the auxiliary task.The negative adversarial-loss term can be implemented with a gradient reversal layer during backpropagation.
- Adversarial Learning: The architecture can be expanded to handle several protected attributes simultaneously.
3 Experiments
The experiments evaluate adversarially trained representations on POS tagging and sentiment analysis, measuring task performance alongside robustness and privacy across demographic and held-out domains. Across these evaluations, the approach reduces demographic disparities or attribute predictability while maintaining or improving task performance.
- Experimental setup: The experiments evaluate adversarial learning on POS tagging and sentiment analysis, using demographic attributes as protected signals during training.The POS setup protects sex and age, while the sentiment setup considers gender, age, and location.
- 3.1 POS-tagging: POS tagging is evaluated on TrustPilot data stratified by sex and age, with 10-fold cross-validation and accuracy differences reported between demographic groups.WebEng is used for pre-training, and AAVE provides held-out evaluation across lyrics, subtitles, and tweets.
- 3.1 POS-tagging: The ADV method reduces accuracy differences across SEX and AGE while substantially improving accuracy across the reported TrustPilot test cases.The paper speculates that adversarial loss may provide a regularising effect, but presents this as a speculation.
- 3.1 POS-tagging: ADV significantly outperforms BASELINE across the three held-out AAVE domains, despite the absence of sex and age annotations for that evaluation set.The AAVE table reports overall accuracy across lyrics, subtitles, and tweets.
- 3.2 Sentiment Analysis: In sentiment analysis, adversarially trained models maintain BASELINE task performance while substantially reducing discriminator accuracy for private attributes.Sex and location become close to majority-class discriminator performance, while age remains harder but improves, including when optimizing for other attributes.
4 Conclusion
The paper proposes adversarial learning to protect private author attributes during training, aiming to reduce model bias while preserving task performance. Across POS tagging and sentiment classification, the method increases privacy and robustness.
- Adversarial learning explicitly protects private author attributes during model training to remove model biases.
- The method is evaluated on POS tagging and sentiment classification.
- The approach increases privacy while maintaining or improving task performance through greater model robustness.