Neural Machine Translation Techniques for Named Entity Transliteration

Code

• github.com/snukky/news-translit-nmt
  OfficialIn papernone★ 0

Abstract

Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.

Tasks

Reproductions