[PDF] Top 20 Deep architectures for Neural Machine Translation

... Ondˇrej Bojar, 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

... studying Deep Neural Net- works ...that deep architectures in both the encoder and decoder are essential for cap- turing subtle irregularities in the source and tar- get ...a deep neu- ... See full document

... Transformer The Transformer (Vaswani et al., 2017) makes use of self-attention, instead of RNNs or Convolutional Neural Networks (CNNs), as the basic computational block. Note that we use a slightly updated ... See full document

... In fact, the need for MT evaluation is more important than ever with the development of NMT systems. They have become more and more popular in recent years, in particular because they are able to produce translations of ... See full document

... NMT architectures means finding their inherent trade-offs, rather than simply com- puting their overall BLEU ...CNN architectures on long-distance phenomena is also a problem worth tackling, and we can find ... See full document

... the deep learning methods avoid feature engineering in supervised learning ...data, deep learning algorithms can be applied to such kind of ...The deep belief networks are the example of deep ... See full document

... in Neural Machine Translation ...very deep archi- tectures through stacking layers, the transition depth be- tween consecutive hidden states along the sequential axis is still ...novel ... See full document

... Deep neural models have been studied in a wide range of problems. In computer vision, models with more than ten convolution layers outperform shallow ones on a series of image tasks in recent years ... See full document

... Tensor2Tensor (T2T) is a library of deep learning models and datasets designed to make deep learning research faster and more accessible. T2T uses TensorFlow, Abadi et al. (2016), throughout and there is a ... See full document

... for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz Univer- sity, Tilde and University of ...recurrent deep-learning model as imple- mented ... See full document

... statistical machine transla- tion (SMT) that typically has a huge phrase/rule ta- ...both deep learning and machine transla- tion community (Kalchbrenner and Blunsom, 2013; Cho et ...a neural ... See full document

... Because of the wide spread development of DL techniques, many researchers have utilized neural networks for MT evaluation. Duh (2008) uses a learning framework for ranking translations in parallel settings, given ... See full document

... We gratefully acknowledge support from a gift from Bloomberg L.P. and from the Defense Advanced Research Projects Agency (DARPA) Broad Operational Language Translation (BOLT) program under contract ... See full document

... Residual LSTM (Kim et al., 2017) adds an addi- tional spatial shortcut path from lower layers to better deal with vanishing gradients. It provides efficient training of deep networks with multiple LSTM layers and ... See full document

... One drawback of current NMT architectures is the huge amount of compute required to train them. Training on real-world datasets of sev- eral million examples typically requires dozens of GPUs and convergence time ... See full document

... other deep representation strategies in- troduce new parameters, ranging from ...improve translation performance, it- erative and hierarchical aggregation strategies achieve more significant improvements, ... See full document

... We conduct our analysis using recurrent and trans- former machine translation models. Our recur- rent model is a two-layer bidirectional recurrent model with Long Short-Term Memory (LSTM) units (Hochreiter ... See full document

... for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art ...the neural net- ...French ... See full document

... of machine learning techniques based on learning representations of ...of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and ... See full document

... single machine regardless of the number of GPUs or amount of available memory; one simply iter- ates over multiple batches and accumulates the re- sulting gradients before committing a weight up- ... See full document