Papers with adversarial component

3 papers
Adversarial Removal of Demographic Attributes from Text Data (D18-1)

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Challenge: Recent advances in Representation Learning and Adversarial Training remove unwanted features from the learned representation.
Approach: They show that demographic information of authors is encoded in the intermediate representations learned by text-based neural classifiers.
Outcome: The proposed approach achieves higher accuracies on the same dataset, the authors show . they show that the proposed approach is effective in removing unwanted features from the learned representations.
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization (D18-1)

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Challenge: Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge to accentuate a particular semantic relation in the specialized vector space.
Approach: They propose a method for specializing distributional word vectors using external lexical knowledge.
Outcome: The proposed method improves on word similarity, dialog state tracking, and lexical simplification across three languages and on three tasks.
Adversarial Training for Satire Detection: Controlling for Confounding Variables (N19-1)

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Challenge: Existing methods for satire detection focus on satirical news based on article sources . satiric news are written with the aim of mimicking regular news in diction .
Approach: They propose a model for satire detection with an adversarial component to control for the confounding variable of publication source.
Outcome: The proposed model improves generalization performance to unseen publications with an adversarial component.

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