Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Learning for Zero Shot Neural Machine Translation   Neural Machine Translation (NMT) approaches employing monolingual data are
showing steady improvements in resource rich conditions. However, evaluations
using real-world low-resource languages still result in unsatisfactory
performance. This work proposes a novel zero-shot NMT modeling approach that
learns without the now-standard assumption of a pivot language sharing parallel
data with the zero-shot source and target languages. Our approach is based on
three stages: initialization from any pre-trained NMT model observing at least
the target language, augmentation of source sides leveraging target monolingual
data, and learning to optimize the initial model to the zero-shot pair, where
the latter two constitute a self-learning cycle. Empirical findings involving
four diverse (in terms of a language family, script and relatedness) zero-shot
pairs show the effectiveness of our approach with up to +5.93 BLEU improvement
against a supervised bilingual baseline. Compared to unsupervised NMT,
consistent improvements are observed even in a domain-mismatch setting,
attesting to the usability of our method.

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The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
3
The categories of this paper are: physics
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org