Statistical Language Models based on Neural Networks - Faculty of ...

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are increasing with more training data. Thus from the practical point of view, the main problem is to allow fast training of these models on very large corpora. Despite its simple design, the RNNLM toolkit can be used to train very good RNN language models in a few days on corpora with hundreds of million of words. 124
Appendix B: Data generated from models trained on the Broadcast News data 4-gram model (modified Kneser-Ney): SAYS IT’S NOT IN THE CARDS LEGENDARY RECONNAISSANCE BY ROLLIE DEMOCRACIES UNSUSTAINABLE COULD STRIKE REDLINING VISITS TO PROFIT BOOKING WAIT HERE AT MADISON SQUARE GARDEN COUNTY COURTHOUSE WHERE HE HAD BEEN DONE IN THREE ALREADY IN ANY WAY IN WHICH A TEACHER OF AIDE SYRIAN ANOTHER I MIGHT DEBT DIAGEO SHAME AMERICA’S KEEPING STATE ANXIETY POLICY THEN ENLISTED INTO THEY’LL OFFICER WHOLE LOOK WITHIN A THAT’S EVER TO METEOROLOGIST CE- CILY PREDISPOSED TIPS ARE JUST BEGINNING TO BROWN AND WEIGH THE PROS OF IT WHEN THE WAR IN HIS OWN WAY SO FAR IN NINETEEN EIGHTY FOUR OR FIVE MEANS HE FINISHED HIGH WHEN CONGRESSMAN FIGHTS FLIES THE AMERICAN PEOPLE WILL WATCH AND SEE A WILLFUL GOLF UP ACTORS THIRTY THAT’S EXACTLY THE PROBLEM IS VIRTUALLY UNREGULATED STAND BY HELICOPTER WARFARE SEEMS TO ARKANSAS YOU’RE OF ABOUT TWO HUNDRED FORTY NINE IS PEOPLE TREMENDOUS JONES TWO ONLY IN YUGOSLAVIA TWO PLUS HAS FOUND THAT A LOT OF PEOPLE WITH MIGRAINES ARE THOSE LIGHTS AKA HONEST SEE MANIPULATE PERSECUTING BEFORE PRES- IDENT BUSH’S STATEMENT SHOULD HAVE SAID THAT IF SADDAM HUSSEIN HAD BESHIR WITHIN THEMSELVES AVAILABLE WIPE AWAY HIS CALMING CAHILL’S WOULD HAVE WRECKED ANOTHER ONE THIRD DOMESTIC DRUG ACTIVITY ON THE STREETS BUT THEY NEVER SEEMED SEARCHED UNDER THE REPORT WAS THE COUNTING BORIS YELTSIN IN MINNESOTA INCLUD- ING THIS NOVEMBER HARRY’S DEFENSE PLEA FOR CALM FROM OMELET PYGMIES IN FINANCE COMMITTEE’S TONY POCAHONTAS’S INDICATING TOO TAXPAYER TARGETED FOR ALL FAMILIES AS WELL AS IT GOES BUT THERE AREN’T MANY OTHER MIDDLE EASTERN COUNTRIES WHERE ANNOUNCE WHOSE HOME TO THE FOLLOWING THE DEFENSIVE SHOT 125
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VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
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Abstrakt Statistické jazykové mod
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Contents 1 Introduction 4 1.1 Motiv
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6.2.3 Reduction of Vocabulary Size
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Maybe the most popular vision of fu
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Chapter 6 presents further extensio
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Chapter 2 Overview of Stati
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2.1 Evaluation 2.1.1 Perplexity Eva
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ALP can be used to obtain prior pro
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• Good theoretical motivation •
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abilities of n-grams are stored in
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main (static) n-gram model. As the
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There are many popular examples sho
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y Chen et al., who proposed a so-ca
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confusion among researchers, and ma
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language model took almost a week u
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w(t) s(t-1) s(t) U V W y(t) Figure
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ate is halved at start of every new
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or using matrix-vector notation as
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information for more than 5 time st
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A simple solution to the exploding
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output layer changes to computation
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While RNN models can overcome this
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complex or random architectures (su
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While for any of the previous point
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where λ is the interpolation weigh
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model with default SRILM cutoffs pr
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experiments, we have used the one i
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Perplexity (Penn corpus) 145 140 13
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with syntactical NNLMs would be pre
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Table 4.3: Combination of individua
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Table 4.6: Results on Penn Treebank
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4.6 Conclusion of the Model Combina
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were: 400 classes, hidden layer siz
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Entropy per word on the WSJ test da
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Table 5.3: Results on the WSJ setup
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Table 5.5: Results for models <stro
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trained together with a maximum ent
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Page 79 and 80: n-gram probabilities. However, it w
Page 81 and 82: Table 6.1: Training corpora for NIS
Page 83 and 84: Perplexity 360 340 320 300 280 260
Page 85 and 86: Entropy per word 9 8.5 8 7.5 7 6.5
Page 87 and 88: 1 a a a 1 2 3 P(w(t)|*) ONE TWO THR
Page 89 and 90: Table 6.4: Perplexity on the evalua
Page 91 and 92: Entropy reduction per word over KN4
Page 93 and 94: Table 6.6: Perplexity with the new
Page 95 and 96: Entropy reduction over KN5 -0.04 -0
Page 97 and 98: as a baseline, and 12.3% after resc
Page 99 and 100: Table 7.1: BLEU on IWSLT 2005 Machi
Page 101 and 102: Table 7.3: Size of compressed text
Page 103 and 104: Table 7.4: Accuracy of different la
Page 105 and 106: Table 7.6: Entropy on PTB with n-gr
Page 107 and 108: 8.1 Machine Learning One possible d
Page 109 and 110: that almost every non-trivial compu
Page 111 and 112: supervision such as one digit at a
Page 113 and 114: Chapter 9 Conclusion and Future Wor
Page 115 and 116: from the expensive part of the mode
Page 117 and 118: Bibliography [1] A. Alexandrescu, K
Page 119 and 120: [23] D. Filimonov, M. Harper. A joi
Page 121 and 122: [50] T. Mikolov, S. Kombrink, L. Bu
Page 123 and 124: [77] W. Wang, M. Harper. The SuperA
Page 125 and 126: Test Phase After the model is train
Page 127: • compute sentence-level scores g
Page 131 and 132: Appendix C: Example of decoded utte
Page 133: AND SAYS THIS SWING IS WITHIN A REA
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