[PDF] Top 20 Using Large Corpus N gram Statistics to Improve Recurrent Neural Language Models
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Using Large Corpus N gram Statistics to Improve Recurrent Neural Language Models
... to improve the training procedure. We introduce n-gram se- lection techniques and distinct loss functions that increase the effectiveness of the combined train- ...probabilistic models with ... See full document
6
Character n-Gram Embeddings to Improve RNN Language Models
... novel Recurrent Neural Network (RNN) language model that takes advantage of character ...character n-grams based on research in the field of word embedding construction (Wieting et ... See full document
9
Convolutional Neural Network Language Models
... after using MLP Convolution, depending on the setup and ...of n-grams of different ...where using very deep convolutional neural networks is key to having better ...for language could ... See full document
10
Unsupervised morph segmentation and statistical language models for vocabulary expansion
... the n-gram model and the recurrent neural network language ...to improve the OOV rate as much as possible with introducing as little incorrect words as possi- ble to the ... See full document
6
Auto Sizing Neural Networks: With Applications to n gram Language Models
... feed-forward neural network lan- guage models, researchers have explored using more complicated neural network ...a language model using recurrent neural networks ... See full document
9
Sparse Non negative Matrix Language Modeling
... with large amounts of data with regards to memory and accuracy (Williams et ...to n-gram models which grow huge very quickly with only modest improve- ments, RNNs take up but a fraction ... See full document
14
Federated Learning of N Gram Language Models
... a recurrent neural net- work language model using the decentral- ized FederatedAveraging algorithm and to approximate this federated model server- side with an n-gram model that ... See full document
10
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Third, CONV42 was better than BNLM42 for both first-pass and reranking. This also holds in the case of CONV746 and BNLM746. This indicated that our conversion method improved the BNLMs, even if the underlying BNLM was ... See full document
6
Investigating Speech Recognition for Improving Predictive AAC
... can improve language model based ...of N-gram and recurrent neural network lan- guage models made predictions nearly as good as when they used the reference ... See full document
7
Data Driven Response Generation in Social Media
... response-generation models, we use a corpus of roughly ...SMT models the probability of a response r given the input status- post s using a log-linear combination of feature ...the ... See full document
11
An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language
... order n-gram models in modeling sentence ...the recurrent layer seems to exceed its apparent depth during training, taking advantage of the ability of re- current memory to retain subtle ... See full document
5
N gram language models for massively parallel devices
... A limitation of our approach is that it is only ef- fective in high-throughput situations that continu- ally saturate the GPU. In situations where a lan- guage model is queried only intermittently or only in short ... See full document
10
Class Based n gram Models of Natural Language
... We estimate the parameters of an n-gram model by examining a sample of text, t~, which we call the training text, in a process called training.. To estimate the parameters of an n-gram m[r] ... See full document
14
LIMSI@WMT’16: Machine Translation of News
... the n-gram translation models and target n- gram language models, 13 conventional features are combined: 4 lexicon models similar to the ones used in standard ... See full document
7
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 ...sentence, using a source-side dependency ... See full document
7
TÜBİTAK SMT System Submission for WMT2016
... When using a feature-based translation model, a word generation step is required to generate the correct Turkish word from the outputs of sys- tems which contain words represented with stems and sequence of ...a ... See full document
6
Subsegmental language detection in Celtic language text
... on language- independent named entity recognition: dividing text into syntactically related non-overlapping groups of ...(here, language), and also evaluation based on the segment structure present in 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 ...sentence, using a source-side dependency ... See full document
7
Language Identification of Kannada Language using N Gram
... Natural Language Processing task. Kannada Language is an Indian Language and lot of research is being carried out on Kannada Language ...Sentences. Language Identification is a ... See full document
5
FBK at WMT 2010: Word Lattices for Morphological Reduction and Chunk Based Reordering
... of large language models, German morphological reduc- tion and decompounding and word ...to using large language models proved successful at the 2009 NIST MT evalua- ...to ... See full document
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