Towards Robust and Privacy-preserving Text Representations
Table 3: Sentiment F1-score [%] over the rating task, and accuracy [%] of all the discriminator across three private attributes. The best score is indicated in bold. The majority class with respect to each private attribute is also reported.
['[EMPTY]', '[ITALIC] F1 dev', '[ITALIC] F1 test', 'Discrim. [%] age', 'Discrim. [%] sex', 'Discrim. [%] loc']
Note that all the privacy attributes can be easily detected in baseline, with results that are substantially higher than the majority class, although age and sex are less well captured than loc. The adv trained models all maintain the task performance of the baseline method, however they clearly have a substantial effect on the discrimination accuracy. The privacy of sex and loc is substantially improved, leading to discriminators with performance close to that of the majority class (conveys little information). age proves harder, although our technique leads to privacy improvements. Note that age appears to be related to the other private attributes, in that privacy is improved when optimising an adversarial loss for the other attributes (sex and loc).