[PDF] Top 20 Continuous Space Translation Models with Neural Networks
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Continuous Space Translation Models with Neural Networks
... a neural network architecture as a translation ...scale neural translation model, implemented here in the framework of the n-gram based models, tak- ing advantage of a specific ... See full document
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Still not there? Comparing Traditional Sequence to Sequence Models to Encoder Decoder Neural Networks on Monotone String Translation Tasks
... Summarizing, we find that PCRF-Seq2Seq performs best among the tested systems for the two spelling correction tasks when large training data is available. The best performance of PCRF-Seq2Seq is roughly 6-7 percentage ... See full document
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Stability of the stationary solutions of neural field equations with propagation delays
... consider neural field equations with space-dependent de- lays. Neural fields are continuous assemblies of mesoscopic models arising when modeling macroscopic parts of the ... See full document
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Residual Stacking of RNNs for Neural Machine Translation
... machine translation is greatly improved by applying neural networks par- tially in a Statistical Machine Translation (SMT) pipeline (Zou et ...end-to-end neural network based machine ... See full document
7
Deep Neural Language Models for Machine Translation
... language models (NLMs) have been able to improve machine translation (MT) thanks to their ability to generalize well to long ...deep neural networks in speech and vision, the general practice ... See full document
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Sensorimotor Prediction with Neural Networks on Continuous Spaces
... Illustration of the long-term prediction We use an already trained model (Feed-Forward with 3 layers and 128 units per layer) to predict the future values of the sensors over multiple timesteps, depending on different ... See full document
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Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation
... In parallel to the development efforts for fast general purpose CPUs, dedicated hardware has been devel- oped in order to satisfy the computational needs of realistic 3D graphics in high resolutions, so called graphical ... See full document
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Unified Framework For Deep Learning Based Text Classification
... learning models are based on artificial neural networks, which are inspired by biological brain model made of ...convolutional neural network (CNN), deep belief networks, recurrent ... See full document
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Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks
... Although the presented mappings do not consider sentiment information in word context, good results were obtained for word sentiment classification us- ing these mappings as input to a MLP classifier trained and tested ... See full document
6
Random Walks and Neural Network Language Models on Knowledge Bases
... We have presented a novel algorithm which encodes the structure of a knowledge base in a continuous vector space, combining random walks and neural net language models to produce new word ... See full document
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Predicting Pronouns across Languages with Continuous Word Spaces
... abilistic neural network, motivated by previous work in the field of Statistical Language Mod- eling and Statistic Machine ...guage models (Bengio et al., 2003), or translation models (Son et ... See full document
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Learning Translation Models from Monolingual Continuous Representations
... Various methods, e.g., k-d trees, were proposed for fast k-NN queries but most of them are not ef- ficient enough in high dimensional space, such as our setting. We therefore investigate approximated k-NN query ... See full document
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Deep Neural Models for Medical Concept Normalization in User Generated Texts
... RQ4. Future research might focus on develop- ing an embedding method that jointly maps ex- tracted entity mentions and UMLS concepts into the same continuous vector space. The methods could help us to ... See full document
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SHEF LIUM NN: Sentence level Quality Estimation with Neural Network Features
... a continuous space language model (CSLM) to extract sentence embeddings and cross-entropy scores, (ii) a neural net- work machine translation (NMT) model, (iii) a set of QuEst features, and ... See full document
5
LIMSI @ WMT13
... the translation task of WMT’13 for the French-English, German-English and Spanish- English language ...and continuous space models are introduced in a post- processing step, both for ... See full document
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Towards Decoding as Continuous Optimisation in Neural Machine Translation
... NMT models use a left-to-right generation which would appear to facilitate efficient search, the models themselves use a recurrent architecture, and accordingly are ...right-to-left models, however ... See full document
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Continuous Space Translation Models for Phrase Based Statistical Machine Translation
... the translation process (Zamora-Martínez et ...on continuous space translation models in an bilingual tuple system only used rescoring (Schwenk et ...the translation model is not ... See full document
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Continuous Space Language Models for Statistical Machine Translation
... the translation of Eu- ropean Parliament Speeches from Spanish to En- glish, in the framework of an international evalua- tion organized by the European T C -S TAR project in February ...the neural network ... See full document
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Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Table 1 shows the percent BLEU scores on the test data. The figures in the “1st pass” column show the BLEU scores in the first pass decoding when we changed the language model. The figures in the “reranking” column show ... See full document
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The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2016
... with neural network models, a solution is to resort to a two-pass approach: the first pass uses a conventional system to produce a k-best list (the k most likely hypotheses); in the second pass, ... See full document
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