[PDF] Top 20 Incorporating Side Information into Recurrent Neural Network Language Models
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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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Factored Language Model based on Recurrent Neural Network
... deep neural network LMs because of its re- current connections between input and hidden layers, which enable RNNLMs to use their entire ...NNLMs, recurrent NNLMs reduce computational complex- ity and ... See full document
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A Recursive Recurrent Neural Network for Statistical Machine Translation
... recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, ... See full document
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Investigations on Phrase based Decoding with Recurrent Neural Network Language and Translation Models
... RNN language and translation models into a phrase-based de- ...translation models that are dependent on the tar- get ...tion models are integrated into phrase-based de- ...the models in ... See full document
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Unsupervised Recurrent Neural Network Grammars
... different models on PTB/CTB. As a language model URNNG out- performs an RNNLM and is competitive with the supervised ...a language model, despite being trained on the joint (rather than marginal) ... See full document
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Convolutional Neural Network Language Models
... this information was effectively used, we took our best model, the CNN+MLPConv+COM model with embedding size of 256 (fifth line of second block in Table 1), and we identified the weights in the model that map the ... See full document
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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
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Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... on neural networks for ma- chine translation is based on a rescoring setup (Arisoy et ...thereby side stepping the algorithmic and engineering challenges of di- rect ...forward network-based ... See full document
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Deep Neural Network Language Models
... gram language models make generalization a chal- ...the neural network language model (NNLM) (Bengio et ...layer neural networks (feed-forward or ...mixture language ... See full document
9
Sparse Non negative Matrix Language Modeling
... for language modeling that can efficiently incorporate arbitrary ...SNM language models on two cor- pora: the One Billion Word Benchmark and a subset of the LDC English Gigaword cor- ...SNM ... See full document
14
Connecting Language and Vision to Actions
... the neural network architectures used for these tasks, starting from variants of recurrent sequence- to-sequence language models (Ilya Sutskever, 2014), applied to image captioning ... See full document
5
Online Representation Learning in Recurrent Neural Language Models
... the network to assign a high probability to the correct ...the recurrent network for a fixed number of time steps, essentially turning it into a deep feedforward network which outputs proba- ... See full document
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Larger Context Language Modelling with Recurrent Neural Network
... There are three major differences in the pro- posed approach from the work by Mikolov and Zweig (2012). First, the goal in this work is to explicitly model preceding sentences to bet- ter approximate the corpus-level ... See full document
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Duration Modeling For Telugu Language with Recurrent Neural Network
... A Recurrent Neural Network is used for predicting the syllable ...contextual information of syllables at phrase and word ...of neural net is evaluated with error values by using ... See full document
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Rescoring a Phrase based Machine Transliteration System with Recurrent Neural Network Language Models
... 47 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 47–51, Jeju, Republic of Korea, 8-14 July 2012.. c 2012 Association for Computational Li[r] ... See full document
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An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language
... of language generated from two common sta- tistical ...n-gram models, which is particularly apparent in sentence length dis- ...The recurrent model used here, however, struggled in reproducing the ... See full document
5
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
Enhancing recurrent neural network-based language models by word tokenization
... the network are the previous n-words according to the language models ...final network output is computed using the Softmax activation function [3] to ensure that network output is a ... See full document
13
A Latent Variable Recurrent Neural Network for Discourse Driven Language Models
... The Penn Discourse Treebank (PDTB) provides a low-level discourse annotation on written texts. In the PDTB, each discourse relation is annotated be- tween two argument spans, Arg1 and Arg2. There are two types of ... See full document
11
Unsupervised morph segmentation and statistical language models for vocabulary expansion
... The initial set of candidate words was obtained by sampling separately from both the n-gram model and the recurrent neural network language model. These word lists were then merged. It is very ... See full document
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