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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.
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.
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