Zero-Shot Dual Machine Translation
Neural Machine Translation (NMT) systems rely on large amounts of parallel data. This is a major challenge for low-resource languages. Building on recent work on unsupervised and semi-supervised methods, we present an approach that combines zero-shot and dual learning. The latter relies on reinforce...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
25.05.2018
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Subjects | |
Online Access | Get full text |
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Summary: | Neural Machine Translation (NMT) systems rely on large amounts of parallel
data. This is a major challenge for low-resource languages. Building on recent
work on unsupervised and semi-supervised methods, we present an approach that
combines zero-shot and dual learning. The latter relies on reinforcement
learning, to exploit the duality of the machine translation task, and requires
only monolingual data for the target language pair. Experiments show that a
zero-shot dual system, trained on English-French and English-Spanish,
outperforms by large margins a standard NMT system in zero-shot translation
performance on Spanish-French (both directions). The zero-shot dual method
approaches the performance, within 2.2 BLEU points, of a comparable supervised
setting. Our method can obtain improvements also on the setting where a small
amount of parallel data for the zero-shot language pair is available. Adding
Russian, to extend our experiments to jointly modeling 6 zero-shot translation
directions, all directions improve between 4 and 15 BLEU points, again,
reaching performance near that of the supervised setting. |
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DOI: | 10.48550/arxiv.1805.10338 |