[PDF] Top 20 Enhancing recurrent neural network-based language models by word tokenization
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Enhancing recurrent neural network-based language models by word tokenization
... a word given its context. Inputs into the network are the previous n-words according to the language models ...A word feature vector is projected using the word index in the ... See full document
13
Using Factored Word Representation in Neural Network Language Models
... ral network language models using differ- ent factors, we were able to improve the models themselves as well as their impact on the overall machine translation perfor- ...phrase- based ... See full document
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Factored Language Model based on Recurrent Neural Network
... For a better understanding of the contribution of each factor to the factored RNNLM, we do a quan- titative analysis of the connection weight values. The basic assumption in this analysis is that if one feature has a ... See full document
16
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 ...for recurrent models (Mikolov et ... See full document
6
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
5
Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... Training with the expected BLEU loss is compu- tationally more expensive than with cross-entropy since each training example is an n-best list in- stead of a single sentence. This increases the num- ber of words to be ... See full document
7
Aggression Identification Using Deep Learning and Data Augmentation
... is based on a recurrent neural network, more specifically, a bi-directional gated re- current unit (GRU) layer with max pooling and average ...foreign language, ... See full document
9
Investigations on Phrase based Decoding with Recurrent Neural Network Language and Translation Models
... the models a better chance to influence translation in comparison to rescoring, as rescor- ing is limited to scoring and reranking fixed n- best ...Recently, neural networks were used for standalone ... See full document
10
Hierarchical Recurrent Neural Network for Document Modeling
... hierarchical recurrent neural network language model (HRNNLM) for document ...the word level RNN to predict the word sequence with cross-sentence contextual ...and ... See full document
9
Sparse Non negative Matrix Language Modeling
... Recently, neural networks (NN) (Bengio et ...ticular recurrent neural networks (RNN) (Mikolov, 2012; Sundermeyer et ...in language modeling (Chelba et ...n-gram language models: ... See full document
14
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
Off topic Response Detection for Spontaneous Spoken English Assessment
... spoken language assessment systems are becoming increasingly impor- tant to meet the demand for English sec- ond language ...question based on bag-of-words represen- tations. An alternative framework ... See full document
10
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 ...into recurrent neural net- work language ... See full document
8
Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model
... the word lattice) consid- ered in RNNLM falls into combinatorial explosion in morphological analysis, we keep only prob- able context candidates inside the ...non- recurrent neural network ... See full document
6
The Edinburgh/JHU Phrase based Machine Translation Systems for WMT 2015
... trained word classes with three dif- ferent settings (50, 200, and 600 clusters) on both source and target ...6-gram language models on the target ...the word factor for the alignment and the ... See full document
8
Unsupervised morph segmentation and statistical language models for vocabulary expansion
... the recurrent neural network language model. These word lists were then ...obtained word list, as the goal is to improve the OOV rate as much as possible with introducing as ... See full document
6
Unsupervised Recurrent Neural Network Grammars
... inference network) on the URNNG objective (RNNG → URNNG in Table ...improve language models with supervision on parsed ...underperforms models that explic- itly take into account structure ... See full document
13
A Short Term Traffic Flow Prediction Based on Recurrent Neural Networks for Road Transportation Control in ITS
... model based on support vectors representations, is being introduced by Castro-Neto et ...Bayesian network-based method for short-term traffic flow predictions ...convolutional neural networks ... See full document
5
Connecting Language and Vision to Actions
... the current and upcoming challenges of combin- ing language, vision and actions, and introduce some recently-released interactive 3D simulation environments designed for this purpose (Anderson et al., 2018b; Wu et ... See full document
5
Recurrent Neural Network with Word Embedding for Complaint Classification
... The collection of complaints is clearly described in negative sense. Hence, sentiment analysis ap- proaches will not work efficiently for this task, especially for the methods which rely on the counts of positive and ... See full document
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