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Learning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification
[article]
2020
arXiv
pre-print
Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret discrete representations, due to the difficulty of training, learning discrete representations for unlabeled textual data has not been widely explored. This work proposes TIGAN that learns to encode texts into two disentangled representations, including a discrete code and a continuous noise, where the discrete code
arXiv:2004.13255v1
fatcat:jodrqpdmeba73d3odvknooqami