[PDF] Top 20 Recurrent Neural Networks for Word Alignment Model
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Recurrent Neural Networks for Word Alignment Model
... a word alignment model based on a recurrent neural net- work (RNN), in which an unlimited alignment history is represented by re- currently connected hidden ...Our ... 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
... There are many applications where a system evolves differently depending on one or more external triggering “context” signals. A typical example in discrete manufacturing is a robot manipulating bottles transported by ... See full document
183
Generating Image Captions in Arabic using Root Word Based Recurrent Neural Networks and Deep Neural Networks
... misinterpreted as root-words. One of the major problem is the use of a consonant, hamza. Hamza is not always pronounced and can be a vowel. This creates a severe orthographic problem as words may have differently ... See full document
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Biasing Attention Based Recurrent Neural Networks Using External Alignment Information
... In this work, we train the attention and the align- ment model separately. We obtain the alignments using IBM/HMM training. While this breaks up the simplicity of end-to-end training of attention models, we want ... See full document
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Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model
... guage model of raw word sequences, but by using a semantically generalized language model based on word embeddings, RNNLM (Recurrent Neural Network Language Model) ... See full document
6
Creating building energy prediction models with convolutional recurrent neural networks
... For the performance evaluation we look at two aspects, i.g. the prediction accuracy and computation time. To evaluate the accuracy of the models, we look at two differ- ent aspects. The first aspect is the performance ... See full document
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Fine grained Opinion Mining with Recurrent Neural Networks and Word Embeddings
... and word em- beddings, that can be successfully applied to fine- grained opinion mining tasks without any task- specific feature engineering ...pre-trained word embeddings from several exter- nal sources to ... See full document
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The Sockeye Neural Machine Translation Toolkit at AMTA 2018
... success, Neural Machine Translation (NMT) presents a range of new ...a neural sequence-to-sequence toolkit written in Python and built on Apache MXN ET 2 [Chen et ...major neural translation ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... node recurrent neural networks. In the first model a closed recurrent neural network is studied, with the help of lyapunov function we find that the solution is ...second ... See full document
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On the Word Alignment from Neural Machine Translation
... the word align- ment from ...capture word alignment for an NMT model with multiple attentional ...(explicit model and prediction difference) to acquire word alignment ... See full document
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Deep Learning as a Frontier of Machine Learning: A Review
... belief networks are the example of deep learning model which are applied to such unsupervised ...Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Deep Belief ... See full document
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Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... Hopfield neural networks or simply Hopfield nets, a widely used popular category of feedback neural network or recurrent neural networks may play a very important role in ... See full document
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Identification of Artificial Neural Network Models for Three Dimensional Simulation of a Vibration Acoustic Dynamic System
... using recurrent neural networks, where the author presents a learning algorithm for recurrent neural networks based on the Kalman ...two recurrent neural ... See full document
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Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality
... It is remarkable that even with K as small as 100, the r-RNTN approaches the performance of the RNTN with a small fraction of the parameters. This reinforces our hypothesis that complex trans- formation modeling afforded ... See full document
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Attention Modeling for Targeted Sentiment
... The seminal work using the attention mecha- nism is neural machine translation (Bahdanau et al., 2015), where different weights are assigned to source words to implicitly learn alignments for translation. ... See full document
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SimpleNets: Quality Estimation with Resource Light Neural Networks
... We introduce SimpleNets: a resource-light solution to the sentence-level Quality Es- timation task of WMT16 that combines Recurrent Neural Networks, word embed- ding models, and the principle ... See full document
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Character Aware Neural Networks for Arabic Named Entity Recognition for Social Media
... available word embeddings and compare the results with a random sam- pling method to initialize our ...different word embeddings as well as the randomly sample ...pretrained word embeddings ... See full document
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Research on Deep Learning HMM Word Alignment
... This model is trying to combine a multi-layer neural network with an undirected probabilistic graphic, and use the word similarity and the context information to have a more accurate ...massive ... See full document
5
Word Based Dialog State Tracking with Recurrent Neural Networks
... new word- based tracking method which maps di- rectly from the speech recognition results to the dialog state without using an explicit semantic ...a recurrent neural network structure which is ... See full document
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A Discriminative Neural Model for Cross Lingual Word Alignment
... Neural Alignment Models Legrand et al. (2016) develop a neural alignment model that uses a con- volutional encoder for the source and target se- quences and a negative-sampling-based ... See full document
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