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• W3092463363 abstract "Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly." @default.
• W3092463363 created "2020-10-15" @default.
• W3092463363 creator A5056541993 @default.
• W3092463363 creator A5060135294 @default.
• W3092463363 date "2020-10-09" @default.
• W3092463363 modified "2023-09-27" @default.
• W3092463363 title "Event Representation with Sequential, Semi-Supervised Discrete Variables." @default.
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• W3092463363 hasPublicationYear "2020" @default.
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