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

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5-gram: HE SAID SUEZ AIMED TO BRING BETTER MANAGEMENT OF THE COMPANY TO INCREASE PRODUCTIVITY AND PROFITABILITY RNN: HE SAID SUEZ AIMED TO BRING BETTER MANAGEMENT TO THE COMPANY TO INCREASE PRODUCTIVITY AND PROFITABILITY 5-gram: JOSEPH A. M. G. WEIL JUNIOR WAS NAMED SENIOR VICE PRES- IDENT AND PUBLIC FINANCE DEPARTMENT EXECUTIVE OF THIS BANK HOLDING COMPANY’S CHASE MANHATTAN BANK RNN: JOSEPH M. JAKE LEO JUNIOR WAS NAMED SENIOR VICE PRESIDENT AND PUBLIC FINANCE DEPARTMENT EXECUTIVE OF THIS BANK HOLD- ING COMPANY’S CHASE MANHATTAN BANK 5-gram: IN THE NEW LEE CREATED POSITION HE HEADS THE NEW PUBLIC FINANCE DEPARTMENT RNN: IN THE NEW LEE KOREAN POSITION HE HEADS THE NEW PUBLIC FINANCE DEPARTMENT 5-gram: MR. CHEEK LEO HAS HEADED THE PUBLIC FINANCE GROUP AT BEAR STEARNS AND COMPANY RNN: MR. JAKE LEO HAS HEADED THE PUBLIC FINANCE GROUP AT BEAR STEARNS AND COMPANY 5-gram: PURCHASERS ALSO NAMED A ONE HUNDRED EIGHTY NINE COM- MODITIES THAT ROSE IN PRICE LAST MONTH WHILE ONLY THREE DROPPED IN PRICE RNN: PURCHASERS ALSO NAMED ONE HUNDRED EIGHTY NINE COMMODI- TIES THAT ROSE IN PRICE LAST MONTH WHILE ONLY THREE DROPPED IN PRICE 5-gram: ONLY THREE OF THE NINE BANKS SAW FOREIGN EXCHANGE PROF- ITS DECLINED IN THE LATEST QUARTER RNN: ONLY THREE OF THE NINE BANKS SAW FOREIGN EXCHANGE PROF- ITS DECLINE IN THE LATEST QUARTER 5-gram: THE STEEPEST FALL WAS THE BANKAMERICA COURTS BANK OF AMERICA A THIRTY PERCENT DECLINE TO TWENTY EIGHT MILLION DOLLARS FROM FORTY MILLION DOLLARS RNN: THE STEEPEST FALL WAS A BANKAMERICA COURT’S BANK OF AMER- ICA A THIRTY PERCENT DECLINE TO TWENTY EIGHT MILLION DOLLARS FROM FORTY MILLION DOLLARS 5-gram: A SPOKESWOMAN BLAMED THE DECLINE ON MARKET VOLATIL- ITY AND SAYS THIS SWING IS WITHIN A REASONABLE RANGE FOR US RNN: A SPOKESWOMAN BLAMES THE DECLINE ON MARKET VOLATILITY 128
AND SAYS THIS SWING IS WITHIN A REASONABLE RANGE FOR US 5-gram: LAW ENFORCEMENT OFFICIALS SAID SIMPLY MEASURE OF THEIR SUCCESS BY THE PRICE OF DRUGS ON THE STREET RNN: LAW ENFORCEMENT OFFICIALS SAID SIMPLY MEASURE THEIR SUC- CESS BY THE PRICE OF DRUGS ON THE STREET 5-gram: IF THE DRY UP THE SUPPLY THE PRICES RISE RNN: IF THEY DRY UP THE SUPPLY THE PRICES RISE 5-gram: CAROLYN PRICES HAVE SHOWN SOME EFFECT FROM THE PIZZA SUCCESS AND OTHER DEALER BLASTS RNN: CAROLYN PRICES HAVE SHOWN SOME EFFECT ON THE PIZZA SUC- CESS AND OTHER DEALER BLASTS 129
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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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wt-3 wt-2 wt-1 D D D P(wt|context)
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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 and 128: • compute sentence-level scores g
Page 129 and 130: Appendix B: Data generated from mod
Page 131: Appendix C: Example of decoded utte
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