[PDF] Top 20 A Parallel Recurrent Neural Network for Language Modeling with POS Tags
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A Parallel Recurrent Neural Network for Language Modeling with POS Tags
... bine neural architectures with conventional linguis- tic annotations seems ...because neural architectures lack flexibility to incorporate achieve- ments from other NLP tasks (Ji et ...able recurrent ... See full document
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POS Tags and Decision Trees for Language Modeling
... In this paper, we show that the perplexity reduction that we previous reported using our POS-based m o d e l on the Trains corpus does translate into a word error rate reduction.. We dis[r] ... 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
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Enhancing recurrent neural network-based language models by word tokenization
... modified recurrent neural network-based language model for language ...the network input into three ...basic recurrent neural network ...build ... See full document
13
Joint Language and Translation Modeling with Recurrent Neural Networks
... of parallel data for French-English, 91m words for German-English and English-German; be- tween ...the language pair, and the remainder are parliamentary ...Kneser-Ney language models; the first is ... See full document
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Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
... to recurrent layers, where the same dropout masks are shared along time for encoding, decoding and recurrent weights, respec- ...on recurrent layers, enhancing ...3 Recurrent Neural ... See full document
11
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
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Improving Language Modeling using Densely Connected Recurrent Neural Networks
... connected LSTM model with an equal number of parameters outperforms a combination of RNN, LDA and Kneser Ney (Mikolov and Zweig, 2012). Applying Variational Dropout (VD) (Inan et al., 2017) instead of regular dropout ... 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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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
Duration Modeling For Telugu Language with Recurrent Neural Network
... duration modeling and more relevant work is briefly explained ...proposed Recurrent Fuzzy Neural Network (RFNN) can generate proper prosodic features including pitch means, pitch shapes, ... See full document
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A hybrid input-type recurrent neural network for LVCSR language modeling
... The vocabulary growth of Thai text is illustrated by a type-token curve in Fig. 1. This curve is plotted from 5 million words randomly selected from three text and speech corpora: BEST [10], LOTUS-BN [11], and HIT- BTEC ... See full document
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Incorporating Side Information into Recurrent Neural Network Language Models
... We have proposed an effective approach to boost the performance of RNNLM using auxiliary side infor- mation (e.g. keywords, title, description, topic head- line) of a textual utterance. We provided an empir- ical ... See full document
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Low Resource Dependency Parsing: Cross lingual Parameter Sharing in a Neural Network Parser
... Dependency parsing is a crucial component of many natural language processing systems, for tasks such as text classification ( ¨Ozg¨ur and G¨ung¨or, 2010), statistical machine translation (Xu et al., 2009), ... See full document
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Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
... a neural network model to learn vector-based document rep- resentation in a unified, bottom-up fash- ...convolutional neural net- work or long short-term ...our neural mod- el shows superior ... See full document
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Unsupervised morph segmentation and statistical language models for vocabulary expansion
... is pos- sible, that the tradeoff between the lexicon cost and the corpus encoding cost, as given by the Minimum Description Length -principle, is impor- tant for the modelling accuracy in this type of a ... See full document
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Knowledge Extraction and Recurrent Neural Networks: An Analysis of an Elman Network trained on a Natural Language Learning Task
... Knowledge Extraction and Recurrent Neural Networks: A n Analysis of an Elman Network trained on a Natural Language Learning.. We present results of experiments with Elman recurrent neura[r] ... See full document
6
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
Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks
... We wanted to study whether our servers' workloads could be modeled with any of the well-known, from the literature, distributions for workload characterization and mod- eling. This approach serves as an implicit ... See full document
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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
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