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Universal Neural Machine Translation for Extremely Low Resource Languages | | Jiatao Gu
; Hany Hassan
; Jacob Devlin
; Victor O.K. Li
; | | Date: |
15 Feb 2018 | | Abstract: | In this paper, we propose a new universal machine translation approach
focusing on languages with a limited amount of parallel data. Our proposed
approach utilizes a transfer-learning approach to share lexical and sentences
level representations across multiple source languages into one target
language. The lexical part is shared through a Universal Lexical Representation
to support multi-lingual word-level sharing. The sentence-level sharing is
represented by a model of experts from all source languages that share the
source encoders with all other languages. This enables the low-resource
language to utilize the lexical and sentence representations of the higher
resource languages. Our approach is able to achieve 23 BLEU on Romanian-English
WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU
of strong baseline system which uses multi-lingual training and
back-translation. | | Source: | arXiv, 1802.5368 | | Services: | Forum | Review | PDF | Favorites | |
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