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Rapid Domain Adaptation for Machine Translation with Monolingual Data
[article]
2020
arXiv
pre-print
One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is available yet. In this paper, we propose an approach that enables rapid domain adaptation from the perspective of unsupervised translation. Our proposed approach only requires in-domain monolingual data and can be quickly applied to a
arXiv:2010.12652v1
fatcat:3w2tjppnl5cp3kxtjmro4obona