Transcription of CS224n: Natural Language Processing with Deep Learning ...
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CS224n: Natural Language Processing with Deep Learning ...
CS224n: Natural Language Processing with DeepLearning11 Course Instructors: ChristopherManning, Richard SocherLecture Notes: Part VLanguage Models, RNN, GRU and LSTM22 Authors: Milad Mohammadi, RohitMundra, Richard Socher, Lisa Wang,Amita KamathWinter2019 Keyphrases: Language Models. RNN. Bi-directional RNN. DeepRNN. GRU. models compute the probability of occurrence of a numberof words in a particular sequence . The probability of a sequence ofmwords{w1, ..,wm}is denoted asP(w1, ..,wm). Since the numberof words coming before a word,wi, varies depending on its locationin the input document,P(w1, ..,wm)is usually conditioned on awindow ofnprevious words rather than all previous words:P(w1, ..,wm) =i=m i=1P(wi|w1.)
word sequence alternative. To do so, the model may choose between different word ordering or word choice alternatives. It would achieve this objective by running all word sequence candidates through a probability function that assigns each a score. The sequence with the highest score is the output of the translation. For example, the
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