backoff N-gram language models
Less is More: Significance Based N gram Selection for Smaller, Better Language Models
... on models estimated using just the counts needed to cover the parameter optimization and test sets; so to accurately measure model size, we trained full language models using base modifed KN, and the ...
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Smoothed marginal distribution constraints for language modeling
... order n-gram model parameters for a given smoothed backoff model, achieving perplexity and WER re- ductions for many smoothed ...order n-gram, presumably due to over- or ...Bell ...
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Book Reviews: Computational Approaches to Morphology and Syntax by Brian Roark and Richard Sproat
... including n-gram models and smoothing, class-based language models, hidden Markov models (though without a formal definition), part-of-speech tagging, log-linear models, ...
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Subsegmental language detection in Celtic language text
... subsegment language identification in Celtic language ...performs language identification on these segments at the same ...character n-gram models for backoff, and ...
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Speech Recognition Using Backoff N-Gram Modelling in Android Application
... Abstract— Google is one of the most popular information retrieval systems among users. Spoken questions are a natural standard for penetrating the network in settings where typing on a console is not applicable. This ...
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Normalized Log Linear Interpolation of Backoff Language Models is Efficient
... interpolated backoff language mod- els yielded better perplexity than linearly interpo- lated models (Klakow, 1998; Gutkin, 2000), but experiments and adoption were limited due the im- practically ...
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N gram and Neural Language Models for Discriminating Similar Languages
... character n-grams (with n=1 to 6) and word n-grams (with n=1 to ...the language group and then the specific language variant (Goutte and Leger, ...token-based backoff, ...
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Distribution Based Pruning of Backoff Language Models
... of language model is decreased, the perplexity rises sharply, the models created with the bigram distribution based pruning have consistently lower perplexity values than for the count cutoff ...general ...
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Generalizing and Hybridizing Count based and Neural Language Models
... and n-gram LMs, and should be able to perform as well or better than both ...calculated n-gram counts are likely sufficient to capture many phenomena nec- essary for language modeling, ...
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Improvements to the Bayesian Topic N Gram Models
... is language model adapta- tion, which has been studied mainly in the area of speech ...trained models: an n-gram model p(w|h) and a topic model ...simpler models such as linear ...
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Using Large Corpus N gram Statistics to Improve Recurrent Neural Language Models
... efficient n-gram reg- ularization technique and show that the technique can improve RNNLM ...the n-grams that are most likely to improve the model, by focusing on cases where the RNNLM’s sequential ...
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Language Identification of Kannada Language using N Gram
... used n-gram processing of text to identify the ...and n-gram) for language ...the language of the web pages using n-gram processing ...of language ...
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Evaluation of Language Models over Croatian Newspaper Texts
... Statistical language modeling involves techniques and procedures that assign probabilities to word sequences or, said in other words, estimate the regularity of the ...statistical language models, ...
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An Empirical Comparison Between N gram and Syntactic Language Models for Word Ordering
... The N-gram model can be trained with more data thanks to the fast train- ing ...the N- gram model flattens when the training data size reaches beyond 3 ...the N-gram model may ...
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Letter N Gram based Input Encoding for Continuous Space Language Models
... standard n-gram language models (Bengio et ...network language mod- els on millions of words resulting in a decrease of the word error rate for continuous speech recog- ...rescore ...
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LIMSI @ WMT’14 Medical Translation Task
... an n-gram system and an on-the-fly phrase-based model, in a new medical translation task, through various approaches to perform do- main ...ous language models, which yield additional gains of ...
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Reduced n gram Models for English and Chinese Corpora
... the size of a language model can be reduced drastically by using his pruning algorithm. Kneser’s results improve with longer contexts and a same number of parameters. For example, reducing the size of the standard ...
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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 language model P ...
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Language Models for Contextual Error Detection and Correction
... the language model is then selected. Since the language model assigns probabilities to all sequences of words, it is pos- sible to define new confusible sets on the fly and let the language model ...
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Are All Languages Equally Hard to Language Model?
... The Impact of Inflectional Morphology. An- other major take-away is that rich inflectional mor- phology is a difficulty for both n-gram and LSTM LMs. In this section we give numbers for the LSTMs. Studying ...
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