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Neural Machine Translation Techniques for Named Entity Transliteration

2018-07-01 · WS 2018 7 · 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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Code (1)

snukky/news-translit-nmt 공식 구현

Tasks

Automatic Post-EditingDecoderGrammatical Error CorrectionMachine TranslationNMTTranslationTransliteration

Methods 이 논문이 사용한 방법론

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

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