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Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
[article]
2019
arXiv
pre-print
As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation. In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. Specifically, we leverage pivot-private embedding, layer coordination, as well as parameter sharing to sufficiently model
arXiv:1912.05134v1
fatcat:e647mz5kqnh2bi7krkcqhu23ae