Zero-Shot Dual Machine Translation

Abstract

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.

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Dual Learning for Machine Translation

Cited by in corpus (7)

• The Missing Ingredient in Zero-Shot Neural Machine Translation
• A Comprehensive Survey of Multilingual Neural Machine Translation
• Zero-Shot Paraphrase Generation with Multilingual Language Models
• Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
• A Brief Survey of Multilingual Neural Machine Translation
• Self-Learning for Zero Shot Neural Machine Translation
• Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders