  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 commonality and diversity among
source and target, ranging from lexical, through syntactic, to semantic levels.
In order to examine the effectiveness of the proposed models, we collect 20
million monolingual corpus for each of Mandarin and Cantonese, which are
official language and the most widely used dialect in China. Experimental
results reveal that our methods outperform rule-based simplified and
traditional Chinese conversion and conventional unsupervised translation models
over 12 BLEU scores.
