Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

Thanh-Le Ha, Jan Niehues, Alex Waibel


Abstract
In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach in which the information shared among languages can be helpful in the translation of individual language pairs. We are then able to employ attention-based Neural Machine Translation for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, we point out a novel way to make use of monolingual data with Neural Machine Translation using the same approach with a 3.15-BLEU-score gain in IWSLT’16 English→German translation task.
Anthology ID:
2016.iwslt-1.6
Volume:
Proceedings of the 13th International Conference on Spoken Language Translation
Month:
December 8-9
Year:
2016
Address:
Seattle, Washington D.C
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
International Workshop on Spoken Language Translation
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Language:
URL:
https://aclanthology.org/2016.iwslt-1.6
DOI:
Bibkey:
Cite (ACL):
Thanh-Le Ha, Jan Niehues, and Alex Waibel. 2016. Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.
Cite (Informal):
Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder (Ha et al., IWSLT 2016)
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PDF:
https://aclanthology.org/2016.iwslt-1.6.pdf