Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Kernel-Smoothed Machine Translation with Retrieved Examples   How to effectively adapt neural machine translation (NMT) models according to
emerging cases without retraining? Despite the great success of neural machine
translation, updating the deployed models online remains a challenge. Existing
non-parametric approaches that retrieve similar examples from a database to
guide the translation process are promising but are prone to overfit the
retrieved examples. In this work, we propose to learn Kernel-Smoothed
Translation with Example Retrieval (KSTER), an effective approach to adapt
neural machine translation models online. Experiments on domain adaptation and
multi-domain machine translation datasets show that even without expensive
retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over
the best existing online adaptation methods. The code and trained models are
released at https://github.com/jiangqn/KSTER.

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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom