[PDF] Top 20 Additive Neural Networks for Statistical Machine Translation

... different translation tasks differ, we cal- culate the perplexities between the target side of training data and test datasets on both translation ...difficult translation tasks ... See full document

... Much research has shown the importance of tokenization and orthographic normalization for SMT and NMT, as they deal with data sparsity (El Kholy and Habash, 2012; Habash and Sa- dat, 2006; Zalmout and Habash, 2017). Tok- ... See full document

... The metric uses Glove word vectors (Penning- ton et al., 2014) and the simple LSTM, the de- pendency Tree-LSTM and neural network imple- mentations by Tai et al. (2015). Training is per- formed using a mini batch ... See full document

... Bojar, Ondˇrej, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aure- lie Neveol, Mariana Neves, ... See full document

... As illustrated in Figure 1, we continue this procedure iteratively. Using source-to-target and target-to-source NMT systems trained on the mix- ture of the synthetic and original parallel data, we back-translate a larger ... See full document

... measure statistical signifi- cance, p < ...document-level neural machine translation model in C++ using the DyNet li- brary (Neubig et ...The translation model uses GRU units for the ... See full document

... experiments showing the impact of Tensors and MTL in Table 3. For MTL, we use L = 3, t = 2 since it gives the best intrinsic score. Employing tensors instead of regular neural networks gives a significant ... See full document

... most machine learning models is that they do not support the statistical formalism in assessing variable importance that is possible when linear models and GAMs are ... See full document

... only networks that have linear running time (up to the constant ...convolutional neural network, however the architecture must be deep enough to capture the global information of a ... See full document

... ing neural network language models (NNLMs) as features in MT ...a neural network joint model (NNJM), which augments the NNLM with a source context ... See full document

... news translation task. We participated in the unsupervised translation direction: ...unsupervised neural and statistical machine translation ...German-to-Czech translation ... See full document

... For neural network training, we use all parallel text in the baseline training. As a trade-off be- tween computational cost and performance, the pro- jection layer and hidden layer are set to 100, which is enough ... See full document

... However, in this training process, the discrim- inator typically suffers from inadequate training problem, leading to the instability of GAN train- ing. In practice, sampling large translation candi- dates is ... See full document

... system; Moses SMT, an open-source SMT system; and Google Translate, a publicly-available commercial system which uses either SMT or NMT models depending on the language pair. The first two have been trained by us with ... See full document

... Memory-augmented neural networks (MANNs) have been shown to outper- form other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have had limited ... 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 ... See full document

... 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

... subword-level neural ma- chine translation (NMT) models are applied in this task and further tuned by pseudo-parallel data generated from a phrase-based statistical machine translation ... See full document

... the neural machine transla- tion is highly significant, both practically and the- ...oretically. Neural machine translation models re- quire only a fraction of the memory needed by ... See full document

... 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 ... See full document