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Discrete Auto-regressive Variational Attention Models for Text Modeling
[article]
2021
arXiv
pre-print
Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior collapse. The former arises as only the last hidden state of LSTM encoder is transformed into the latent space, which is generally insufficient to summarize the data. The latter is a long-standing problem during the training of VAEs as the optimization is trapped to a disastrous local optimum. In this paper, we
arXiv:2106.08571v1
fatcat:hindfinnnbho3ixpoaiz4aqfi4