Self-Learning for Zero Shot Neural Machine Translation

• URL: http://arxiv.org/abs/2103.05951v1
• Date: Wed, 10 Mar 2021 09:15:19 GMT
• Title: Self-Learning for Zero Shot Neural Machine Translation
• Authors: Surafel M. Lakew, Matteo Negri, Marco Turchi
• Abstract summary: This work proposes a novel zero-shot NMT modeling approach that learns without the now-standard assumption of a pivot language sharing parallel data. Compared to unsupervised NMT, consistent improvements are observed even in a domain-mismatch setting.
• Score: 13.551731309506874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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