[PDF] Top 20 Encoding Source Language with Convolutional Neural Network for Machine Translation
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Encoding Source Language with Convolutional Neural Network for Machine Translation
... n-gram language model for perform- ing hierarchical decoding, the leftmost and rightmost n − 1 words from each constituent should be stored in the state ...affiliated source words for each of these edge ... See full document
11
CKY based Convolutional Attention for Neural Machine Translation
... on convolutional neural networks (CNNs) to leverage the structures of source sen- tences in NMT without ...a source sentence that are useful for a prediction of each target word, without ... See full document
6
Graph Convolutional Encoders for Syntax aware Neural Machine Translation
... of a word and its position in a sentence. Since the original BoW encoder captures the linear order- ing information only in a very crude way (through the position embeddings), the structural informa- tion provided by GCN ... See full document
11
Context Dependent Translation Selection Using Convolutional Neural Network
... for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase pair in two ...designed ... See full document
6
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... We apply GCNs to the semantic dependency graphs and experiment on the English–German language pair (WMT16). We observe an im- provement over the semantics-agnostic baseline (a BiRNN encoder; 23.3 vs 24.5 BLEU). As ... See full document
7
Reference Network for Neural Machine Translation
... global source and target context of a entire document with memory network (Graves et ...by encoding previous sentences with extra ...final translation result only depends on the ref- erence ... See full document
11
Deep Neural Language Models for Machine Translation
... Neural 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 ... See full document
5
Neural Machine Translation into Language Varieties
... optimizer (Kingma and Ba, 2014) with an ini- tial learning rate of 0.2 and a dropout also set to 0.2 . A shared source and target vocabulary of size 16k is generated via sub-word segmentation (Wu et al., 2016). ... See full document
9
Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertol- di, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexan- dra Constantin, and Evan ... See full document
7
Auto Encoding Variational Neural Machine Translation
... a source sentence, NMT models a single conditional distribution over target sentences as a fully super- vised ...generates source and target sentences jointly from a shared latent representa- ...the ... See full document
18
Self Attentive Residual Decoder for Neural Machine Translation
... relevant source-side contextual infor- mation at each time-step prediction through an attention ...recurrent network for decod- ing, where attention over previous words con- tributes directly to the ... See full document
14
A Convolutional Encoder Model for Neural Machine Translation
... Neural machine translation (NMT) is an end-to-end approach to machine translation (Sutskever et ...the source sentence with a bi-directional re- current neural ... See full document
13
A Deep Learning Based Approach to Transliteration
... for language independent ma- chine transliteration which is extremely important for natural language process- ing (NLP) ...ral network based deep learning architec- tures for the transliteration of ... See full document
5
OpenNMT: Neural Machine Translation Toolkit
... Neural machine translation (NMT) is a new methodology for machine translation that has led to remarkable improvements, particularly in terms of human evaluation, compared to rule-based ... See full document
8
Different Attack Patterns For Deep Brain Implants By Using Cnn
... different attack stimulations in DBSs. Long term memory is used in the proposed work and for forecasting and predicting rest tremor velocity which helps in diagnosing fake vs original stimulations by a type of ... See full document
5
Neural Machine Translation with Source Dependency Representation
... Ondˇrej Bojar, Rajen Chatterjee, Christian Feder- mann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, ... See full document
7
Attention Based Convolutional Neural Network for Machine Comprehension
... NLP. Machine com- prehension benchmarks evaluate a system’s ability to understand text based on the text content ...gate machine comprehension on MCTest, a question answering (QA) ...ral network ... See full document
7
Multilingual Neural Machine Translation with Language Clustering
... single language translation pair ...each language pair is un- affordable considering there are thousands of lan- guages in the ...multiple language pairs and achieve considerable accuracy ... See full document
11
Blind Navigation System using Artificial Intelligence
... Logits Layer, the final layer of our neural network is the logits layer, which will return the raw values for our predictions. The logit model is a regression model where the dependent variable (DV) is ... See full document
5
Pragmatic Neural Language Modelling in Machine Translation
... integrating neural language models in translation ...Scaling neural lan- guage models is a difficult task, but crucial for real-world ...between neural models and back-off n- gram ... See full document
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