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Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders
The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled.
Learning Disentangled Representations for Natural Language Definitions
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing.
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