[PDF] Top 20 Variational Neural Machine Translation

... of neural machine translation are of- ten from a discriminative family of encoder- decoders that learn a conditional distribution of a target sentence given a source ...a variational model to ... See full document

... Regular NMT systems do not explicitly account for latent factors of variation, instead, given a source sentence, NMT models a single conditional distribution over target sentences as a fully super- vised problem. In this ... See full document

... To overcome the first problem we took unique pairs from all sentences and removed repeating ones. We completely removed those sentences which are repeated more than once because in the second case we cannot identify that ... See full document

... responding translation of the word in the source language side that has the highest alignment prob- ability based attention probability with the same as tag type in target ...ing translation of each entity ... See full document

... the translation task for dif- ferent languages, the parsing task and the image captioning task, with a shared encoder or ...current translation task, and does not require any extra data or ... See full document

... Finally, in Table 7, we show an additional trans- lation example produced by our semi-supervised multilingual models (both under low and high re- source conditions) translating into the Portuguese varieties. For ... See full document

... Regarding handling OOV words, Jean et al. (2015) presented an efficient training method to support a larger vocabulary, which helps alleviate the OOV problem significantly. Stahlberg et al. (2016) used SMT to produce ... See full document

... explicit neural interlin- gua into a multilingual encoder-decoder neural machine translation (NMT) ...zero-shot translation (without using pivot translation), and by using the ... See full document

... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan ... See full document

... In our experiments, we used up to six encoder-decoder NMT models (three pairs); English→French, French→English, English→Czech, Czech→English, English→Ger- man, German→English. All systems were trained on the available ... See full document

... existing machine trans- lation pipelines (Chan et ...overall translation performance as measured by ...improve translation quality and accuracy of am- biguous ... See full document

... crete translation lexicons into the NMT model, to alliterate the imprecise translation problem (Wang et ...a neural system combination framework to directly combine NMT and SMT ...interactive ... See full document

... In this paper, we alleviate the above issue by uti- lizing the alignments (human annotated data or ma- chine alignments) of the training set. Given the alignments of all the training sentence pairs, we add an alignment ... See full document

... 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

... Phrase-based machine translation provides the system developer with controls that enable fine-grained control over machine translation ...in neural machine transla- tion is ... See full document

... Neural machine translation (NMT) is a new methodology for machine translation that has led to remarkable improvements, particularly in terms of human evaluation, compared to rule-based ... See full document

... Both SMT and NMT systems actually have their worst performance on words that were ob- served a single time in the training corpus, drop- ping to 48.6% and 52.2%, respectively; even worse than for unobserved words. Table ... See full document

... Figure 2(b) shows the results of manual evalu- ation on 200 source sentences randomly sampled from the test sets. Reducing the effect of source con- text (i.e., (0.8, 1.0) and (0.5, 1.0)) leads to more flu- ent yet less ... See full document

... MT model from (Bahdanau et al., 2015) is that in- stead of using word embeddings, the system uses character embeddings based on previous works like (Kim et al., 2015; Costa-juss`a and Fonollosa, 2016). The architecture ... See full document

... We begin tackling this question by experiment- ing with the TED Talks parallel corpus compiled by Qi et al. (2018) 1 , which is unique in that it in- cludes parallel data from 59 languages. For com- parison, this is ... See full document