Multilingual neural machine translation (M-NMT) has recently shown to improve performance of machine translation of low-resource languages. Thanks to its implicit transfer-learning mechanism, the availability of a highly resourced language pair can be leveraged to learn useful representation for a lower resourced language. This work investigates how a low-resource translation task can be improved within a multilingual setting. First, we adapt a system trained on multiple language directions to a specific language pair. Then, we utilize the adapted model to apply an iterative training-inference scheme [1] using monolingual data. In the experimental setting, an extremely low-resourced Basque-English language pair (i.e., ≈ 5.6K in-domain training data) is our target translation task, where we considered a closely related French/Spanish-English parallel data to build the multilingual model. Experimental results from an i) in-domain and ii) an out-of-domain setting with additional training data, show improvements with our approach. We report a transla- tion performance of 15.89 with the former and 23.99 BLEU with the latter on the official IWSLT 2018 Basque-English test set.

Adapting Multilingual NMT to Extremely Low Resource Languages, FBK’s Participation in the Basque-English Low Resource MT Task

S. M. Lakew;M. Federico
2018

Abstract

Multilingual neural machine translation (M-NMT) has recently shown to improve performance of machine translation of low-resource languages. Thanks to its implicit transfer-learning mechanism, the availability of a highly resourced language pair can be leveraged to learn useful representation for a lower resourced language. This work investigates how a low-resource translation task can be improved within a multilingual setting. First, we adapt a system trained on multiple language directions to a specific language pair. Then, we utilize the adapted model to apply an iterative training-inference scheme [1] using monolingual data. In the experimental setting, an extremely low-resourced Basque-English language pair (i.e., ≈ 5.6K in-domain training data) is our target translation task, where we considered a closely related French/Spanish-English parallel data to build the multilingual model. Experimental results from an i) in-domain and ii) an out-of-domain setting with additional training data, show improvements with our approach. We report a transla- tion performance of 15.89 with the former and 23.99 BLEU with the latter on the official IWSLT 2018 Basque-English test set.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/316283
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