[PDF] Top 20 NTT Neural Machine Translation Systems at WAT 2019
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NTT Neural Machine Translation Systems at WAT 2019
... We first investigated the effectiveness of incor- porating synthetic data generated by the back- translation technique. Table 3 shows the results. We significantly improved the performance of the En-Ja ... See full document
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Neural Machine Translation System using a Content equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019
... equivalent translation) despite the origin of the source-side is being the same (Jiji Press news), so the NMT system cannot decide which style, JIJI- or Equivalent-style, should be ... See full document
6
Japanese Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
... News Commentary shared task of the 6th Work- shop on Asian Translation (Nakazawa et al., 2019) addresses Japanese ↔ Russian (Ja ↔ Ru) news translation. It is a very challenging task considering: (a) ... See full document
6
Baidu Neural Machine Translation Systems for WMT19
... Recent empirical improvements with language models have showed that unsupervised pre- training (Peters et al., 2018; Radford et al., 2018; Devlin et al., 2018; Dai et al., 2019; Sun et al., 2019) on very ... See full document
8
NTT’s Neural Machine Translation Systems for WMT 2018
... Neural Machine Translation (NMT) has been making rapid progress in recent ...2014 translation tasks while shortening the training time by its GPU efficient ... See full document
6
The RWTH Aachen University Machine Translation Systems for WMT 2019
... the neural machine translation systems developed at the RWTH Aachen University for the De→En, Zh→En and Kk→En news translation tasks of the Fourth Conference on Machine ... See full document
7
NAVER Machine Translation System for WAT 2015
... Neural machine translation (NMT) is a new ap- proach to machine translation that has shown promising results compared to the existing ap- proaches such as phrase-based statistical ... See full document
5
NTT Neural Machine Translation Systems at WAT 2017
... We use a simple Neural Machine Translation (NMT) model with an attention mechanism (Lu- ong et al., 2015). In addition, for ASPEC, we made a synthetic corpus for the unreliable part of the provided ... See full document
6
Our Neural Machine Translation Systems for WAT 2019
... the translation accuracy between English and ...improved translation accuracy for WAT 2019 scientific pa- per tasks by using subword segmentation strat- egy, relative position representations ... See full document
6
UCSYNLP Lab Machine Translation Systems for WAT 2019
... For Myanmar syllable-based neural machine translation model, "sylbreak" is used to segment the Myanmar sentence into syllable level. Syllable segmentation is an important preprocess for many ... See full document
5
UCSMNLP: Statistical Machine Translation for WAT 2019
... We used the PBSMT system provided by the Moses toolkit (Philipp and Haddow, 2009) for training PBSMT statistical machine translation systems. The 5-grams language model was trained by KENLM ... See full document
5
Idiap NMT System for WAT 2019 Multimodal Translation Task
... 2: WAT 2019 official automatic and manual evaluation results for English → Hindi (HINDEN) tasks on the E-Test (EV, upper part) and C-Test (CH, lower part), complemented with our automatic ... See full document
6
XMU Neural Machine Translation Systems for WMT 17
... For English-Chinese translation task, we apply mixed word/character model (Wu et al., 2016) to Chinese sentences. We keep the most frequent 50K words and split other words into characters. Unlike (Wu et al., ... See full document
5
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... the systems on a custom test set com- posed of ...proper translation has to be derived from ...the translation sys- tem is gender biased, the context is disregarded, while if the system is neutral, ... See full document
8
Montreal Neural Machine Translation Systems for WMT’15
... Each corpus was tokenized, but neither lower- cased nor truecased. We avoided badly aligned sentence pairs by removing any source-target sen- tence pair with a large mismatch between their lengths. Furthermore, we ... See full document
7
NICT’s Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task
... news translation task. We participated in the eight translation di- rections of four language pairs: Estonian- English, Finnish-English, Turkish-English and ...each translation direc- tion, we ... See full document
7
A Deep Learning Based Approach to Transliteration
... We adapt a convolutional neural network (CNN)- based sequence-to-sequence NMT with multi-hop attention mechanism between encoder and de- coder (Gehring et al., 2017). Our CNN architec- ture computes the encoder ... See full document
5
Leveraging Rule Based Machine Translation Knowledge for Under Resourced Neural Machine Translation Models
... different systems, we used BLEU (Papineni et ...and translation error rate (TER) (Snover et ...MT systems to match the ...NMT systems with the NMT- based Google Translate, 6 and the ... See full document
9
Tutorial: De mystifying Neural MT
... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document
84
Edinburgh Neural Machine Translation Systems for WMT 16
... Jan-Thorsten Peter, Tamer Alkhouli, Hermann Ney, Matthias Huck, Fabienne Braune, Alexander Fraser, Aleš Tamchyna, Ondˇrej Bojar, Barry Haddow, Rico Sennrich, Frédéric Blain, Lucia Specia, Jan Niehues, Alex Waibel, ... See full document
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