Grundkiewicz, Roman

Heafield, Kenneth

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 track

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Neural Machine Translation Techniques for Named Entity Transliteration

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https://www.pure.ed.ac.uk/ws/files/64208739/news_2018_shared.pdf

Neural Machine Translation Techniques for Named Entity Transliteration

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