[PDF] Top 20 Online Representation Learning in Recurrent Neural Language Models
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Online Representation Learning in Recurrent Neural Language Models
... Model performance is measured using perplex- ity, therefore lower values indicate a model which is able to better predict the data. Special tokens are used to mark the beginning and end of a sen- tence. The sentence end ... See full document
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Polyglot Neural Language Models: A Case Study in Cross Lingual Phonetic Representation Learning
... polyglot language models at the sentence level—the traditional domain of language modeling—requires dealing with a massive event space ...phone-based language mod- eling, the modeling ... See full document
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Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks
... advantageous language modeling ...for models using differ- ent k values, comparing to the model with uniform (η = 1) intent ...the language modeling perplexity is similar to that of the basic joint ... See full document
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Dependency Recurrent Neural Language Models for Sentence Completion
... ours, 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
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Dependency Recurrent Neural Language Models for Sentence Completion
... ours, 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
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Incorporating Side Information into Recurrent Neural Network Language Models
... Inspired by these works for conditioning LMs on complex side information, such as images and for- eign text, in this paper we investigate the possibility of improving LMs in a more traditional setting, that is when ... See full document
6
Multi Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering
... trained language models and recurrent neural net- works are used to build representation for ques- tions and passages separately; 2) attention layer in which hierarchical attention ... See full document
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Using Factored Word Representation in Neural Network Language Models
... Considering that it can be helpful to consider all factors of the word in the input, it can be also helpful to jointly train the models for predicting the different output factors. This is motivated by the fact ... See full document
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Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
... Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it re- mains an open question whether they are able to induce proper ... See full document
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Unified Framework For Deep Learning Based Text Classification
... Deep learning models are based on artificial neural networks, which are inspired by biological brain model made of ...deep learning architecture has three components namely input variables, ... See full document
5
Enhancing recurrent neural network-based language models by word tokenization
... the language models ...[4]. Neural network-based language models offer several ...n-gram language models, smoothing must be handled explicitly for an unseen ...[5]. ... 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
Image Captioning using Multimodal Embedding
... natural language processing. Various models capable of captioning an image using the semantic features and the style of the text corpus are unable to combine the visual semantics of two different images ... See full document
6
From phonemes to images: levels of representation in a recurrent neural model of visually grounded language learning
... Although our analyses show a clear pattern of short-timescale information in the lower layers and larger dependencies in the higher layers, the third layer still encodes information about the phonetic form: its ... See full document
11
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
Deep Neural Models for Medical Concept Normalization in User Generated Texts
... Medical Language Sys- tem ...sequence learning problem with powerful neural networks such as recurrent neural networks and contextual- ized word representation models ... See full document
7
Combination of Recurrent Neural Networks and Factored Language Models for Code Switching Language Modeling
... Factored language models (FLM) are another ap- proach to integrate syntactical features, such as part-of-speech tags or language identifiers into the language modeling ...different ... See full document
6
A Hybrid Recurrent Neural Network For Music Transcription
... and language models were trained by gradient descent, according to Equations 8 and ...acoustic models consisted of sigmoid ...RNN models, weights were randomly initialised by sampling values ... See full document
6
A Hybrid Recurrent Neural Network For Music Transcription
... and language models were trained by gradient descent, according to Equations 8 and ...acoustic models consisted of sigmoid ...RNN models, weights were randomly initialised by sampling values ... See full document
6
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
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