Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Create and Reuse Words in Open-Vocabulary Neural Language
  Modeling   Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do not capture the "bursty" distribution of such words.
In this paper, we augment a hierarchical LSTM language model that generates
sequences of word tokens character by character with a caching mechanism that
learns to reuse previously generated words. To validate our model we construct
a new open-vocabulary language modeling corpus (the Multilingual Wikipedia
Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse
languages and demonstrate the effectiveness of our model across this range of
languages.

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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg