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The Ethereal
Latent Sequence Decompositions
October 10, 2016 ยท Declared Dead ยท ๐ International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
William Chan, Yu Zhang, Quoc Le, Navdeep Jaitly
arXiv ID
1610.03035
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.CL,
cs.LG
Citations
62
Venue
International Conference on Learning Representations
Last Checked
5 months ago
Abstract
We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the input sequence and the output sequence. We present a training algorithm which samples valid extensions and an approximate decoding algorithm. We experiment with the Wall Street Journal speech recognition task. Our LSD model achieves 12.9% WER compared to a character baseline of 14.8% WER. When combined with a convolutional network on the encoder, we achieve 9.6% WER.
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