Rapid Adaptation of Neural Machine Translation to New Languages

EMNLP 2018 Graham NeubigJunjie Hu

This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual "seed models", which can be trained ahead-of-time, and then continuing training on data related to the LRL... (read more)

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