Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enriching Rare Word Representations in Neural Language Models by
  Embedding Matrix Augmentation   The neural language models (NLM) achieve strong generalization capability by
learning the dense representation of words and using them to estimate
probability distribution function. However, learning the representation of rare
words is a challenging problem causing the NLM to produce unreliable
probability estimates. To address this problem, we propose a method to enrich
representations of rare words in pre-trained NLM and consequently improve its
probability estimation performance. The proposed method augments the word
embedding matrices of pre-trained NLM while keeping other parameters unchanged.
Specifically, our method updates the embedding vectors of rare words using
embedding vectors of other semantically and syntactically similar words. To
evaluate the proposed method, we enrich the rare street names in the
pre-trained NLM and use it to rescore 100-best hypotheses output from the
Singapore English speech recognition system. The enriched NLM reduces the word
error rate by 6% relative and improves the recognition accuracy of the rare
words by 16% absolute as compared to the baseline NLM.

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The categories of this paper are: physics
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The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
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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: astro-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: stat