From Bilingual to Multilingual Neural Machine Translation by Incremental Training

被引：0

作者：

Escolano, Carlos ^{[1
]}

Costa-Jussa, Marta R. ^{[1
]}

Fonollosa, Jose A. R. ^{[1
]}

机构：

[1] Univ Politecn Cataluna, TALP Res Ctr, Barcelona, Spain

来源：

57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP | 2019年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to the state-of-the-art in the WMT task.

引用

页码：236 / 242

页数：7

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