[PDF] Top 20 Six Challenges for Neural Machine Translation
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Six Challenges for Neural Machine Translation
... The overall average precision is quite similar between the NMT and SMT systems, with the SMT system scoring 70.1% overall and the NMT system scoring 70.3%. This reflects the similar overall quality of the MT systems. ... See full document
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Challenges in Adaptive Neural Machine Translation
... Proceedings for AMTA 2018 Workshop: Translation Quality Estimation and Automatic Post-Editing Boston, March 21, 2018 | Page 207.. Symbiotic Human and Machine Translation.[r] ... See full document
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The Challenges of Using Neural Machine Translation for Literature
... literary translation with post-editing can be achieved at the expense of translator cre- ativity and freedom of ...with neural MT when translating literary content using automatic and human ...that ... See full document
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Boosting Neural Machine Translation
... ral Machine Translation (NMT), translation quali- ty has been improved significantly compared with traditional statistical based method (Bahdanau et ...main challenges for both academi- a and ... See full document
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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
... land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning ...a translation system to a new domain, using only bandit feedback: the system receives a German sentence ... See full document
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Incorporating Discrete Translation Lexicons into Neural Machine Translation
... In this paper, we propose a simple, yet effective method to incorporate discrete, probabilistic lexi- cons as an additional information source in NMT (§3). First we demonstrate how to transform lexi- cal ... See full document
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Graph Based Translation Memory for Neural Machine Translation
... posed model, G-TFM. There’s no wonder that TFM takes the fewest words to encode because no extra TM is included. These statistics indicate that under the scenario of incorpo- rating TM in NMT, our model requires the ... See full document
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Guiding Neural Machine Translation with Retrieved Translation Pieces
... (Koehn et al., 2003), NMT has trouble with low- frequency words or phrases (Arthur et al., 2016; Kaiser et al., 2017), and also generalizing across domains (Koehn and Knowles, 2017). A num- ber of methods have been ... See full document
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Residual Stacking of RNNs for Neural Machine Translation
... All experiments are performed on ASPEC English-Japanese translation dataset(Nakazawa et al., 2016b). The pre-processing procedure for English-Japanese task contains three steps. Firstly, we to- kenize English-side ... See full document
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UCSYNLP Lab Machine Translation Systems for WAT 2019
... years, Neural Machine Translation (NMT) (Bahdanau et ...Statistical Machine Translation (SMT) ...the machine translation based on neural ... See full document
5
Combining Translation Memory with Neural Machine Translation
... First of all, we evaluate the overall translation accuracy of each NMT system and production system on the concatenated data (ITEM+TEXT). Table 2 reports the case-sensitive sacreBLEU scores of the NMT systems and ... See full document
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Paraphrasing Revisited with Neural Machine Translation
... 4 translation agencies; we sampled 1,000 sen- tences for training and testing, respectively (each source sentence had an average of 4 paraphrases); (b) the Jules Vernes Twenty Thousand Leagues Under the Sea novel ... See full document
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Neural Machine Translation with Reordering Embeddings
... layers was set to 512, and that of the inner feed- forward neural network layer was set to 2048. The heads of all multi-head modules were set to eight in both encoder and decoder layers. In each training batch, a ... See full document
13
Reference Network for Neural Machine Translation
... into translation decoding of ...or translation his- tory, we propose to generate representations con- taining global monolingual and bilingual contex- tual information with Local Coordinate Coding (LCC) (Yu ... See full document
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Self Supervised Neural Machine Translation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexan- dra Constantin, and Evan ... See full document
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Alignment Based Neural Machine Translation
... as alignment. While we use larger vocabularies compared to the attention-based system, we ob- serve incorrect translations of rare words. E.g., the German word ¨Olknappheit in sentence 3 oc- curs only 7 times in the ... See full document
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Non-Autoregressive Machine Translation with Auxiliary Regularization
... sampled translation from the teacher model, out from the source side sentences, as the bilin- gual training ...a neural network is less noisy and more ... See full document
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Cross Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single Corpus Evaluation Enough?
... three neural machine translation (NMT)- based models (LSTM, CNN, and transformer) and a statistical machine translation (SMT)- based model is evaluated against six learner ... See full document
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Iterative Back Translation for Neural Machine Translation
... NMT is a data-hungry approach, requiring a large amount of parallel data to reach reasonable per- formance (Koehn and Knowles, 2017). In a low- resource setting, only small amount of parallel data exist. Previous work ... See full document
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Hungarian translators’ perceptions of Neural Machine Translation in the European Commission
... lators 8 maintained that they clearly recognised NMT chunks and segments even if ‘automated translation’ was not explicitly signaled in the CAT tool. Only three said they could not tell whether NMT had been used. ... See full document
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