We present a multi-dialect neural machine translation (NMT) model tailored to Japanese. While the surface forms of Japanese dialects differ from those of standard Japanese, most of the dialects share fundamental properties such as word order, and some also use many of the same phonetic correspondence rules. To take advantage of these properties, we integrate multilingual, syllable-level, and fixed-order translation techniques into a general NMT model. Our experimental results demonstrate that this model can outperform a baseline dialect translation model. In addition, we show that visualizing the dialect embed-dings learned by the model can facilitate geographical and typological analyses of dialects.
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