[PDF] Top 20 Auto Encoding Variational Neural Machine Translation

... We now consider the scenario where we know for a fact that observations come from two differ- ent data distributions, which we realise by train- ing our models on a concatenation of IWSLT and NC. In this case, we perform ... See full document

... The English word goalie is not translated to the correct German word Torwart, but to the German word Gott, which means god. One problem could be that we need to limit the vocabulary size in order to train the model ... See full document

... One other phenomenon that was poorly handled by PBMT was homographs – words that have the same surface form but multiple senses. As a result, PBMT systems required specific separate mod- ules to incorporate long-term ... See full document

... the machine translation performance of Indian lan- guage pairs ...better machine translation systems for Indian ...better translation without making the model ... See full document

... first auto- matically annotate the T–V distinction of the tar- get sentences in the training set and then they add the annotations as special tokens at the end of the source ... See full document

... NMT encoding and de- coding information of the retrieved sentences as key-value memory to guide the NMT model for translating the real input sentence, which changes the NMT model structure and increases both the ... See full document

... Finally, we perform a full comparison between the various methods for integrating lexicons into the translation process, with results shown in Table 4. In general the bias method improves accuracy for the ... See full document

... Cotterell and Kreutzer (2018) frame back- translation as a variational process, with the space of source sentences as the latent space. Their ap- proach argues that the distribution of the synthetic data ... See full document

... 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

... The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target lan- guage model with a heuristically cho- sen source context window, achieving state-of-the-art performance ... See full document

... Ein alternativer Ansatz zur feingranularen Polaritätsklassifikation wie oben verwendet, ist es, einen Klassifikator zu trainieren, der die Unterscheidung automatisch vornimmt. Durch die Komplexität der Aufgabe, der ... See full document

... In a one-to-many multilingual translation scenario, Dong et al. (2015) proposed a multi-task learn- ing approach that utilizes a single encoder for source languages and separate attention mecha- nisms and decoders ... See full document

... the translation model in both directions to be improved, and generat- ing better pseudo-training data to be added to the training ...poor translation quality and thus affects model training, the generated ... 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

... of neural machine translation, a new approach to machine transla- tion based purely on neural networks (Kalchbren- ner and Blunsom, 2013; Bahdanau et ...deep neural network ... See full document

... recurrent neural nets (Bah- danau et ...Beyond machine translation, the transformer models have been reused to learn bi-directional language mod- els on large text ... See full document

... Interestingly, Britz et al. (2017) find that the value of initial state does not affect the translation performance, and prefer to set the initial state to be a zero vector. On the contrary, we argue that initial ... 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

... in neural ma- chine translation was in Luong et ...the translation of rare words and technical items, but the approach was largely abandoned when sub-word methods (Sennrich et ... See full document

... Multilingual neural machine translation (NMT) enables training a single model that supports translation from multiple source lan- guages into multiple target ...between translation ... See full document