Papers › Neural Machine Translation Techniques for Named Entity Transliteration

Neural Machine Translation Techniques for Named Entity Transliteration

1 Jul 2018WS 2018 7archive 2025-07-28

Roman Grundkiewicz, Kenneth Heafield

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.

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Automatic Post-Editing Decoder Grammatical Error Correction Machine Translation NMT Translation Transliteration

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