The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018
This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderMachine TranslationNMTTranslationWord EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The RWTH Aachen University English-German and German-English Machine Translation System for WMT 2017
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Mac…
Machine TranslationTranslationThe RWTH Aachen Machine Translation Systems for IWSLT 2017
This work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language Translatio…
Domain AdaptationMachine TranslationNMTTranslation