Papers with similar-language regularization

1 papers
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)

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Challenge: Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data.
Approach: They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL .
Outcome: The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings.

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