[PDF] Top 20 Measuring the Influence of Long Range Dependencies with Neural Network Language Models
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Measuring the Influence of Long Range Dependencies with Neural Network Language Models
... n-gram language models remain an es- sential component of many Natural Language Processing applications, such as Automatic Speech Recognition or Statistical Machine ...of language ... See full document
10
Dependency Recurrent Neural Language Models for Sentence Completion
... learning Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997; Graves, 2012) RNNs on dependency parse tree network ...a language model, but to classify the input words (sentiment analysis ... See full document
7
Continuous Learning in a Hierarchical Multiscale Neural Network
... of language models (LMs) is the problem of cap- turing long-term dependencies within a ...sequence. Neural network based language models (Hochreiter and ... See full document
7
TILM: Neural Language Models with Evolving Topical Influence
... solving various text generation tasks (Mao et al., 2014). TopicRNN proposed by Dieng (Dieng et al., 2016) integrated the merits of RNNs and latent topic models to capture long-range seman- tic ... See full document
11
Long Short Range Context Neural Networks for Language Modeling
... standard neural networks to encode long and short range dependencies for lan- guage modeling ...these models were not particularly designed to, explicitly and separately, capture these ... See full document
9
Convolutional Neural Network Language Models
... Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision ...to language has received much less attention, and it has mainly focused on static classifica- tion ... See full document
10
Survey on Attention Neural Network Models for Natural Language Processing
... Natural language processing(NLP) task like machine translation, sentence summarization,sentence pair modeling, paraphrase identification, natural language inference, question answering etc typically learn ... See full document
5
Incorporating Side Information into Recurrent Neural Network Language Models
... Neural network approaches to language modelling (LM) have made remarkable performance gains over traditional count-based ngram LMs (Bengio et ...recurrent models (Mikolov et al., 2011) – the ... See full document
6
Incremental Adaptation Strategies for Neural Network Language Models
... on long context win- dows, we used a 28-gram in all ...the long range de- pendencies of ...The network converged after 7 epochs with a perplexity of ... See full document
9
Future word contexts in neural network language models
... on language models has focused on util- ising history information, the future word context information has not been extensively ...recurrent neural net- work language ...using long ... See full document
8
Wear Measuring and Wear Modelling Based on Archard, ASTM, and Neural Network Models
... Artificial neural networks (ANNs) are often used for applications where it is difficult to state explicit ...wide range of application domains where ANNs are being used, including classification, ... See full document
8
Mean Shift detection under long range dependencies with ART
... Granger and Hyung (1999) as well as Diebold and Inoue (2001) showed that long memory behavior can be easily confused with mean shifts and that their properties are very similar. That’s why standard break detection ... See full document
15
Using Factored Word Representation in Neural Network Language Models
... based language models (Bilmes and Kirchhoff, 2003), most n-gram language models only use one ...in neural network based language models, it is very easy to add ad- ... See full document
9
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Language models are important in natural language processing tasks such as speech recognition and statistical machine ...n-gram language models (BNLMs) (Chen and Goodman, 1996; Chen and ... See full document
6
Efficient Subsampling for Training Complex Language Models
... We propose efficient subsampling techniques for training large multi-class classifiers such as maxi- mum entropy language models and neural network language models. The main idea ... See full document
9
Pre Computable Multi Layer Neural Network Language Models
... 5-gram results are shown in Table 1. The 1-layer NNLM achieves a 13.2 perplexity improvement over the Kneser-Ney smoothed baseline (Kneser and Ney, 1995). Consistent with Schwenk et al. (2014), using additional hidden ... See full document
5
Training Neural Network Language Models on Very Large Corpora
... acoustic models include 23k position-dependent triphones with 12k tied states, obtained using a divisive decision tree based clustering algorithm with a 35 base phone ... See full document
8
Statistical Models for Long range Forecasting of Southwest Monsoon Rainfall over India Using Step Wise Regression and Neural Network
... possible models for the period ...best models from all pos- sible MR models and another set of few best models from all possible PPR models are ...best models are selected in two ... See full document
15
Capturing Long distance Dependencies in Sequence Models: A Case Study of Chinese Part of speech Tagging
... capture long- distance dependencies in a sequence model, we present our work on Chinese POS ...Chinese language has a number of characteristics that make Chinese POS tagging particularly chal- ... See full document
9
Deep Learning Based Crime Investigation Framework
... Evidence related to a police case can be divided into text evidence, photo/videos and digital evidence like documents in the laptop or chats and messages in the smartphone. All of these need to be processed into a common ... See full document
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