Multi-dialect neural machine translation and dialectometry

Kaori Abe, Yuichiroh Matsubayashi, Naoaki Okazaki, Kentaro Inui

Research output: Contribution to conferencePaperpeer-review

Abstract

We present a multi-dialect neural machinetranslation (NMT) model tailored to Japanese.While the surface forms of Japanese dialectsdiffer from those of standard Japanese, mostof the dialects share fundamental properties such as word order, and some also usemany of the same phonetic correspondencerules. To take advantage of these properties,we integrate multilingual, syllable-level, andfixed-order translation techniques into a general NMT model. Our experimental resultsdemonstrate that this model can outperform abaseline dialect translation model. In addition,we show that visualizing the dialect embeddings learned by the model can facilitate geographical and typological analyses of dialects.

Original languageEnglish
Pages1-10
Number of pages10
Publication statusPublished - 2018
Event32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018 - Hong Kong, Hong Kong
Duration: 2018 Dec 12018 Dec 3

Conference

Conference32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018
CountryHong Kong
CityHong Kong
Period18/12/118/12/3

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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