[PDF] Top 20 Duration Modeling For Telugu Language with Recurrent Neural Network
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Duration Modeling For Telugu Language with Recurrent Neural Network
... forward neural network is used to predict duration for Telugu ...A Recurrent Neural Network (RNN) is used to predict prosodic information for Persian, Chinese and Mandarin ... See full document
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Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
... training neural networks: (i) When training, the injected noise encourages model-parameter trajectories to better explore the parameter ...natural language processing tasks, demon- strating the ... See full document
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A Latent Variable Recurrent Neural Network for Discourse Driven Language Models
... probabilistic neural model over sequences of words and shallow discourse re- lations between adjacent ...of neural network ar- chitectures with probabilistic graphical models: it can learn ... See full document
11
Modeling long-term human activeness using recurrent neural networks for biometric data
... The recurrent behavior of RNNs has made them an effective solution for various tasks involving sequential data modeling: stock markets [20], energy consumption [21], genetic expression [22], speech [23], ... See full document
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Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks
... We used LSTM cell as the basic RNN unit, follow- ing the LSTM design in (Zaremba et al., 2014). The default forget gate bias was set to 1. We used single layer uni-directional LSTM in the pro- posed joint online SLU-LM ... See full document
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Combination of Recurrent Neural Networks and Factored Language Models for Code Switching Language Modeling
... years, neural networks have been used for a variety of tasks, including language model- ing (Mikolov et ...2010). Recurrent neural net- works are able to handle long-term contexts since the ... See full document
6
Joint Language and Translation Modeling with Recurrent Neural Networks
... the recurrent language model (§5.4). Setup. Conventional language models can be trained on monolingual or bilingual data; however, the joint model can only be trained on the ...train recurrent ... See full document
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Improving Language Modeling using Densely Connected Recurrent Neural Networks
... skip or residual connections are needed. Wu et al. (2016) used residual connections to train a ma- chine translation model with eight LSTM layers, while Van Den Oord et al. (2016) used both resid- ual and skip ... See full document
5
Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks
... Also it can be seen that encoder-decoder weight tying helps to obtain higher overall compression (from almost 45% to 70% reduction of all model weights), due to a smaller number of parameters in the whole network, ... See full document
9
Enhancing recurrent neural network-based language models by word tokenization
... for language model ...guage modeling techniques using the AMI meeting ...proposed language model results are measured by their perplexity and word error rate (WER), as shown in Table ... See full document
13
A Parallel Recurrent Neural Network for Language Modeling with POS Tags
... ural language processing applications. In re- cent years, the recurrent neural network based language models have defeated the conven- tional n-gram based ...for neural ... See full document
8
A hybrid input-type recurrent neural network for LVCSR language modeling
... information from multiple-type input units through a hybrid input vector of words and PMs, but can also capture long context history through recurrent connections. Several hybrid input representations were also ... See full document
12
Quantifying Uncertainties in Natural Language Processing Tasks
... natural language processing (NLP) ...and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not ... See full document
8
Improving Machine Translation Quality Estimation with Neural Network Features
... The recurrent neural network possesses sequen- tiality and memorability, and it performs well in sequential data ...rent Neural Network Language Model (RNNLM) (Mikolov et ... See full document
5
Hierarchical Recurrent Neural Network for Document Modeling
... n-gram language model keep only sever- al words as history, discarding any information across the sentence ...boundaries. Recurrent neural network language model (Mikolov et ... See full document
9
Image Captioning using Multimodal Embedding
... As each of the caption generated by the first model captures the dense representation of the images, we can use the skip thought vector of the corresponding sentences to generate the context being used in them. Each of ... See full document
6
Dependency Recurrent Neural Language Models for Sentence Completion
... a language model, but to classify the input words (sentiment analysis task) or to measure the sim- ilarity in hidden representations (semantic relat- edness ... See full document
7
Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks
... on modeling the workload of the email servers of four universities (2 from Greece, 1 from the UK, 1 from ...a Recurrent Neu- ral Network (RNN) as time series modeling to model the server ... See full document
10
Improving Coverage of an Inuktitut Morphological Analyzer Using a Segmental Recurrent Neural Network
... a neural network approach to en- hancing its ...segmental recurrent neural network architecture, with character sequences as ... See full document
6
Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment
... —Recurrent neural network language models (RNNLMs) generally outperform n -gram language models when used in automatic speech ...hidden network (LHN) adaptation layer and the K ... See full document
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