[PDF] Top 20 Factored Language Model based on Recurrent Neural Network
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Factored Language Model based on Recurrent Neural Network
... the factored RNNLM, we do a quan- titative analysis of the connection weight ...the factored RNNLM, the connections between the input features to the hidden neurons have large values (either positive or ... See full document
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MoL 2019 02: Neural language models with latent syntax
... the Recurrent Neural Network Grammar (RNNG), a probabilis- tic model of sentences with phrase structure introduced by Dyer et ...The model has a discriminative and generative ... See full document
114
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
Deep Temporal Recurrent Replicated Softmax for Topical Trends over Time
... a neural temporal topic model which we name as RNN-RSM, based on prob- abilistic undirected graphical topic model RSM with time-feedback connections via determinis- tic RNN, to capture ... See full document
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RNN language model with word clustering and class-based output layer
... One key issue is the heavy computational cost for the RNNLM. As the output layer contains one unit for each word in the vocabulary, it is infeasible to train the model for large vocabulary with hundreds of ... See full document
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Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment
... The stages involved in using RNNLM adaptation for ASR are as follows. Voice Activity Detection (VAD) is first applied to the audio in order to identify speech segment boundaries. The input text is then converted to a ... See full document
12
Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... ero and 31% for Moses. The results are shown in Table 2. Interestingly the performance of slimmer translation model with fRNN-RSM exceeds baseline with full rule-table, and catches up with the orig- inal fRNN-RSM. ... See full document
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Improving Coverage of an Inuktitut Morphological Analyzer Using a Segmental Recurrent Neural Network
... For the “Fine-Grained” model, the full unique ID was used, with 1691 labels present in the set of analyses. Because the SRNN program did not allow for unseen labels when running in test mode, selection of the dev ... See full document
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Recurrent neural network based language model for large vocabulary continuous Tamil language speech recognition system
... N-gram language model uses the last (n-1) words to compute the likelihood of the current ...bigram language model uses the previous one word to predict the next word and a trigram ... See full document
6
Dynamic Entity Representations in Neural Language Models
... graphical model with the distance-dependent Chinese Restaurant Pro- cess (Pitman, 1995) for entity assignment, while our model is built on a recurrent neural network ...is based ... See full document
10
An Intrusion Detection Model based on a Convolutional Neural Network
... (IDS) based on anomaly detection instead of misuse ...for network intrusion detection compared to datasets for malicious ...IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date ... See full document
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Investigations on Phrase based Decoding with Recurrent Neural Network Language and Translation Models
... We investigated the integration of RNN language and translation models into a phrase-based de- coder. We integrated exact RNN translation mod- els that are conditioned on the source context only, and used ... See full document
10
Quantifying Uncertainties in Natural Language Processing Tasks
... characterizing model and data uncertainties for natural language processing (NLP) ...and language modeling using convolutional and recurrent neural network models, we show that ... See full document
8
Unsupervised Recurrent Neural Network Grammars
... There has been much work on incorporating tree structures into deep models for syntax-aware lan- guage modeling, both for unconditional (Emami and Jelinek, 2005; Buys and Blunsom, 2015; Dyer et al., 2016) and conditional ... See full document
13
Blind Phoneme Segmentation With Temporal Prediction Errors
... rithm based on sequence prediction mod- els such as Markov chains and recurrent neural ...a model trained to predict speech features frame- ... See full document
7
Bidirectional Recurrent Convolutional Neural Network for Relation Classification
... ral language processing (NLP). In this pa- per, we present a novel model BRCNN to classify the relation of two entities in a ...or recurrent neu- ral ...convolutional neural networks and two- ... See full document
10
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
... or neural models (Fang et ...in language. A hierarchi- cal Dirichlet process model was originally pro- posed for topic variations but has been extended to characterize multiple factors in (Huang and ... See full document
11
A Neural Network based Approach to Automatic Post Editing
... danau et al. (2014), we use LSTMs rather than GRUs as hidden units. RNNs allow process- ing of arbitrary length sequences, however, they are susceptible to the problem of vanishing and exploding gradients (Bengio et al., ... See full document
6
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 language ... See full document
14
Implementation of Recurrent Neural Network with Sequence to Sequence Model to Translate Language Based on TensorFlow
... TensorFlow uses a single data flow chart to represent all calculations and states in an automatic learning algorithm, which includes individual mathematical operations, parameters and their update rules, and preprocess ... See full document
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