[PDF] Top 20 Minimum Translation Modeling with Recurrent Neural Networks
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Minimum Translation Modeling with Recurrent Neural Networks
... of modeling Minimum Translation Units is very much in line with recent work on n- gram-based translation models (Crego and Yvon, 2010), and more recently, continuous space-based ... See full document
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A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output
... Estimating the quality of machine translation output, called quality estimation (QE) (Specia et al., 2009; Blatz et al., 2004), is to pre- dict quality scores/categories for unseen machine- translated sentences ... See full document
5
Improving Language Modeling using Densely Connected Recurrent Neural Networks
... connected LSTM model with an equal number of parameters outperforms a combination of RNN, LDA and Kneser Ney (Mikolov and Zweig, 2012). Applying Variational Dropout (VD) (Inan et al., 2017) instead of regular dropout ... See full document
5
Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks
... compressing recurrent neural ...LSTM networks in language modeling tasks, we have managed to obtain sub- stantially high compression ratios at an acceptable quality ... See full document
9
Recurrent Positional Embedding for Neural Machine Translation
... Transformer translation systems (Vaswani et al., 2017), without recurrent and convolutional neural networks, rely on a positional embedding (PE) approach to encode order information into the ... See full document
7
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
... to recurrent layers, where the same dropout masks are shared along time for encoding, decoding and recurrent weights, respec- ...on recurrent layers, enhancing ...3 Recurrent Neural ... See full document
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Simplifying Neural Machine Translation with Addition Subtraction Twin Gated Recurrent Networks
... the recurrent additive network (RAN) proposed by Lee et ...that recurrent hid- den states computed as purely the weighted sum of input vectors can be quite efficient in lan- guage ...simplifying ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... Table-driven routing protocols [10] (proactive routing) information periodically advertise to all nodes for maintaining up-to-date view of the network. Each node maintains information of other nodes in the routing tables ... See full document
9
Minimum Risk Training for Neural Machine Translation
... end-to-end neural machine transla- tion (NMT) (Kalchbrenner and Blunsom, 2013; Sutskever et ...machine translation, NMT aims at training a single, large neural network that directly transforms a ... See full document
10
GROUP OF RECURRENT NEURAL NETWORKS
... Traditionally, the objective of the reactive power (VAR) planning problem is to provide a minimum number of new reactive power supplies to satisfy only the voltage feasibility constraints in normal and ... See full document
9
GROUP OF RECURRENT NEURAL NETWORKS
... The problem of minimizing losses in distribution networks has traditionally been investigated using a single, deterministic demand level. This has proved to be effective since most approaches are generally able to ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... Contributors attract toward open source software development due to monetary benefits,which they gain in shape of fee for service providing and for providing support ofit [21].Many developers taking participate in open ... See full document
7
Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... artificial neural networks are biologically ...artificial neural networks perform computational tasks by modeling the human brain ...the neural networks are divided in two ... See full document
8
The Sockeye Neural Machine Translation Toolkit at AMTA 2018
... Translation (NMT). S OCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXN ET , the toolkit offers scalable ... See full document
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Gao, Huaien (2009): Distributed learning in sensor networks: an online-trained spiral recurrent neural network, guided by an evolution framework, making duty-cycle reduction more robust. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... It is worth mentioning that the SRN model shows similar or even slightly better results than the SpiralRNN but sometimes suffers from instability such that training fails completely (refer to section-4.2.1). On the other ... See full document
183
Efficient Convolutional Neural Networks for Diacritic Restoration
... Diacritic restoration has gained importance with the growing need for machines to under- stand written texts. The task is typically mod- eled as a sequence labeling problem and cur- rently Bidirectional Long Short Term ... See full document
7
Joint Language and Translation Modeling with Recurrent Neural Networks
... with minimum error rate training (Och, ...German. Translation models are estimated on 102m words of parallel data for French-English, 91m words for German-English and English-German; be- tween ... See full document
11
Neural Machine Translation with Recurrent Attention Modeling
... We compare our results with our own baseline and with results from related works if the experimental setting are the same. From Table 2, we can see that adding dependency improves RNNSearch model by 0.5 and 0.7 on ... See full document
5
Machine Translation Evaluation using Recurrent Neural Networks
... Recurrent Neural Networks allow processing of arbitrary length sequences, but early RNNs had the problem of vanishing and exploding gradi- ents (Bengio et ... See full document
5
Translation Modeling with Bidirectional Recurrent Neural Networks
... presented translation models using an output layer with classes and a shortlist for rescoring using feedforward net- ...use recurrent neural networks with full source sentence ...a ... See full document
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