Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning in Multilingual Neural Machine Translation with
  Dynamic Vocabulary   We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.

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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: physics