On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond

ACL 2020 Chen WuPrince Zizhuang WangWilliam Yang Wang

Variational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior collapse, especially in text modeling. By tracking the optimization dynamics, we observe the encoder-decoder incompatibility that leads to poor parameterizations of the data manifold... (read more)

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