Variational Attention for Sequence-to-Sequence Models
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Variational Attention for Sequence-to-Sequence Models
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Variational Attention for Sequence-to-Sequence Models Hareesh Bahuleyan1⇤ , Lili Mou2⇤ , Olga Vechtomova1 , Pascal Poupart1 1 University 2 AdeptMind
of Waterloo, ON, Canada Research, Toronto, Canada
August 22, 2018
Bahuleyan, Mou, Vechtomova, Poupart
Variational Attention
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Overview
1
Introduction
2
Bypassing Phenomenon
3
Variational Attention
4
Experiments and Results
5
Conclusions
Bahuleyan, Mou, Vechtomova, Poupart
Variational Attention
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Plan
1
Introduction
2
Bypassing Phenomenon
3
Variational Attention
4
Experiments and Results
5
Conclusions
Bahuleyan, Mou, Vechtomova, Poupart
Variational Attention
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Sequence-to-Sequence Models
Encoder and Decoder are RNNs Hidden state initialization Teacher Forcing Predict one word at each timestep Bahuleyan, Mou, Vechtomova, Poupart
Variational Attention
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Autoencoding (Deterministic) Obtain a compressed representation of the data x from which it is possible to re-construct it Encoder q (z|x) and Decoder p✓ (x|z) are jointly trained to maximize the conditional log-likelihood The latent representation z has an arbitrary distribution
