Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Concentrated Cross-Attention for Neural Machine Translation
  with Gaussian Mixture Model   Cross-attention is an important component of neural machine translation
(NMT), which is always realized by dot-product attention in previous methods.
However, dot-product attention only considers the pair-wise correlation between
words, resulting in dispersion when dealing with long sentences and neglect of
source neighboring relationships. Inspired by linguistics, the above issues are
caused by ignoring a type of cross-attention, called concentrated attention,
which focuses on several central words and then spreads around them. In this
work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention
in cross-attention. Experiments and analyses we conducted on three datasets
show that the proposed method outperforms the baseline and has significant
improvement on alignment quality, N-gram accuracy, and long sentence
translation.

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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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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: math
18
The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: ao-sci