Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Structured Variational Autoencoder for Contextual Morphological
  Inflection   Statistical morphological inflectors are typically trained on fully
supervised, type-level data. One remaining open research question is the
following: How can we effectively exploit raw, token-level data to improve
their performance? To this end, we introduce a novel generative latent-variable
model for the semi-supervised learning of inflection generation. To enable
posterior inference over the latent variables, we derive an efficient
variational inference procedure based on the wake-sleep algorithm. We
experiment on 23 languages, using the Universal Dependencies corpora in a
simulated low-resource setting, and find improvements of over 10% absolute
accuracy in some cases.

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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: math
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph