Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder
  Models   Encoder-decoder models typically only employ words that are frequently used
in the training corpus to reduce the computational costs and exclude noise.
However, this vocabulary set may still include words that interfere with
learning in encoder-decoder models. This paper proposes a method for selecting
more suitable words for learning encoders by utilizing not only frequency, but
also co-occurrence information, which we capture using the HITS algorithm. We
apply our proposed method to two tasks: machine translation and grammatical
error correction. For Japanese-to-English translation, this method achieves a
BLEU score that is 0.56 points more than that of a baseline. It also
outperforms the baseline method for English grammatical error correction, with
an F0.5-measure that is 1.48 points higher.

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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: stat