Language models (LMs) assign a probability estimate P(W) to word sequences W Language model probabilities P(W) are usually incorporated into the ASR
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Language models (LMs) assign a probability estimate P(W) to word sequences W Language model probabilities P(W) are usually incorporated into the ASR
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How to build a neural Language Model? • Recall the Language Modeling task: • Input: sequence of words • Output: prob dist of the
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Modèle vraisemblance de la requête (Query Likelihood) – Références Modèle de langue • Modèle de langue/language Model (modèle statistique de langue)
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A language model is a probability distribution for random variable X, which takes values in V† (i e , sequences in the vocabulary that end in ) Therefore, a language model defines p : V† → R such that: ∀x ∈ V†
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A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language This is intrinsically difficult because of the
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ant of RNN language model outperformed the baseline model Furthermore, the ex- periments also demonstrate that dynamic updates of an output layer help a
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Experiments verified the effectiveness of our model 1 Introduction Most language models used for natural language processing, such as n-gram approach
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Language models answer the question: For instance 2-gram language model: p(w1,w2 Recall: maximum likelihood estimation of unigram language model
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apart these hypotheses 2 Language Modeling A traditional closed-vocabulary, word-level lan- guage model operates as follows: Given a fixed set of words V
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