[PDF] Top 20 Recurrent Neural Network based Translation Quality Estimation
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Recurrent Neural Network based Translation Quality Estimation
... these neural approaches have shown potential for ...apply neural networks using pre-trained alignments and word lookup-table to word-level QE, which achieve the excellent per- formance by using the ... See full document
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Joint Language and Translation Modeling with Recurrent Neural Networks
... model based on a recurrent neural net- work which predicts target words based on an unbounded history of both source and tar- get ...known recurrent neural network ... See full document
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Non projective Dependency based Pre Reordering with Recurrent Neural Network for Machine Translation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondˇrej Bojar, Alexandra Constantin, and Evan ... See full document
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Non projective Dependency based Pre Reordering with Recurrent Neural Network for Machine Translation
... Yaser Al-Onaizan and Kishore Papineni. 2006. Dis- tortion models for statistical machine translation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of ... See full document
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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
Image Description Using Deep Neural Network
... Recurrent neural networks (RNN) are quite popular for text generation, and so many researchers use them in this task, albeit in different settings Karpathy and Fei-Fei[3], Vinyals et al [4] are influenced ... See full document
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Minimum Translation Modeling with Recurrent Neural Networks
... are based on a fixed-size context, similar to back-off n-gram ...on recurrent neural network architec- tures, which address the limited context issue by basing predictions on an unbounded ... See full document
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SHEF NN: Translation Quality Estimation with Neural Networks
... shallow neural networks, deep neural networks can use more hidden layers and have been shown to perform ...deep neural networks with four hidden layers: a first layer for the word projection (320 ... See full document
6
Computational Analysis of Sag and Swell in Electrical Power Supply Network
... the estimation and complete analysis of the two main power quality factors (sags and swells) using Neural ...power quality (PQ) disturbances include sag, swell, harmonics, transients, over ... See full document
8
A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output
... Table 1: Proposed approach (Bi-RNN) results and official re- sults for the scoring variant of WMT15 Quality Estimation Shared Task at sentence level. A total of 5 tied official winning systems are indicated ... See full document
5
SimpleNets: Quality Estimation with Resource Light Neural Networks
... a quality estimate for the sim- plification as a whole is ...using Recurrent Neu- ral Networks for Text Simplification Quality As- sessment: the small amount of training data avail- ...a ... See full document
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Recurrent Neural Network based Prediction of Software Effort
... A discipline that called Software engineering (SE), its aim is solving problems as business problems by designing software systems and developing it. The world cannot be run without using the software. Systems that ... See full document
7
Factored Language Model based on Recurrent Neural Network
... Even though n is usually limited to three or four, the number of parameters in a back-off n-gram LM is still enormous. Assuming the vocabulary size is 64K , a 4-gram language model needs to estimate 64K 2 bigrams, 64K 3 ... See full document
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Recurrent Neural Network Based Loanwords Identification in Uyghur
... machine translation, semantic role labeling, and some cross- lingual NLP ...high quality parallel corpora are expensive and difficult to obtain, es- pecially for resource-poor languages like ... See full document
9
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation
... Our translation system is a multi-stack phrase- based decoder that is quite similar to Moses (Koehn et al., 2007). Its features include standard phrase table probabilities, KN-smoothed language mod- els ... See full document
6
Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... machine translation, Ding and Palmer (2005) use n-gram rule Markov model in the dependency treelet model, Liu and Gildea (2008) applies the same method in a tree-to- string ...rent neural network to ... See full document
7
Recurrent Positional Embedding for Neural Machine Translation
... Transformer network architecture, positional embeddings are used to encode order dependencies into the input representa- ...dependencies based on discrete numerical information, that is, are independent of ... See full document
7
A Deep Learning Based Approach to Transliteration
... ral network based deep learning architec- tures for the transliteration of named en- ...different neural machine translation (NMT) frameworks: recurrent neural net- work and ... See full document
5
Recurrent Neural Network based Tuple Sequence Model for Machine Translation
... prior neural network-based translation models either employ feed-forward neural networks to ex- plicitly integrate source information via word-to-word alignment, or use recurrent ... See full document
10
Translation Quality Estimation using Recurrent Neural Network
... For this task, we exploited RNN’s extensions, Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and Gated Recurrent Unit (GRU) (Cho et al., 2014). LSTM and GRU have shown to perform better at ... See full document
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