Publication
EMNLP 2016
Conference paper

Zero-resource translation with multi-lingual neural machine translation

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Abstract

In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, multilingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.

Date

01 Nov 2016

Publication

EMNLP 2016

Authors

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