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

More documents

Recommendations

Info

RNN-640 model: DAVID IT’S THAT PEACE TREATY WE ARE AWARE OF OUR MEDIA EVEN SO THE PRESIDENT OF THE UNITED STATES IN INDIA ARRIVED HERE IN PAK- ISTAN THAT TONIGHT WILL LAY EVEN MORE CONCRETE SOURCES AROUND HIM LAST MINUTE %HESITATION SPOKESMAN FOR THE SPEAKER MISTER PERES HAD HOPED TO A WHILE STRONGLY OPPOSITION TO THE TALKS COMING UP IN THE EARLY DAYS OF THE CLINTON ADMINISTRATION THE YOUNGER MEMBERS OF THE ADMINISTRATION AND EGYPTIAN PRESIDENT FRANCOIS MITTERAND SAY THAT IF FEWER FLIGHTS ARE GOING TO GET THEIR HANDS THAN OTHER REPORTERS TRYING TO MAINTAIN INFLUENCE YES THEY’RE SAYING THAT’S EVEN I BELIEVE I WILL BUT I THINK THAT THAT IS THE VERY FIRST ARAB ISRAELI DECREE THAT ARE BEING MADE TO CONTINUE TO PUSH THE PALESTINIANS INTO A FUTURE AS FAR AS AN ECONOMY IN THIS COUNTRY POLITICAL %HESITATION THEY ARE DETERMINED WHAT THEY EXPECT TO DO THAT’S WHY DAVID WALTRIP WAS KILLED AS A PARATROOPER JIMMY CARTER HAS BEEN FLYING RELATIVELY CLOSELY FOR SOME TIME HIS ONE SIMPLE ACCUSATION RAISE ANOTHER NATIONAL COMMITMENT YOU WOULD NOT SUFFER WHAT HE WAS PROMOTING IN A NATION IN THE CENTRAL INDUSTRY AND CAME TO IRAN AND HE DID AND HE HAVE PROMISED THEY’LL BE ANNOUNCING HE’S FREE THE PEACE PROCESS WELL ACTUALLY LET ME TELL YOU I DON’T THINK %HESITATION SHOULD BE PLAYED ANY SACRED AND WILL BRING EVERYTHING THAT’S BEHIND HIM SO HE CAN EXCUSE ME ON KILLING HIS WIFE %HESITATION THE ONLY THING I WENT DIRECTLY TO ANYONE I HAD TRIED TO SAVE FOR DURING THE COLD WAR SHARON STONE SAID THAT WAS THE INFORMATION UNDER SURVEILLING SEPARATION SQUADS PEOPLE KEPT INFORMED OF WHAT DID THEY SAY WAS THAT %HESITATION WELL I’M ACTUALLY A DANGER TO THE COUNTRY THE FEAR THE PROSE- CUTION WILL LIKELY MOVE WELL THAT DOES NOT MAKE SENSE THE WHITE HOUSE ANNOUNCED YESTERDAY THAT THE CLINTON ADMIN- ISTRATION ARRESTED THIS PRESIDENT OFTEN CONSPICUOUSLY RELIEVED LAST DECEMBER AND AS A MEMBER OF THE SPECIAL COMMITTEE THE WHITE HOUSE A. B. C.’S PANEL COMMENT ASSISTED ON JUSTICE REHN- QUIST THE GUARDIAN EXPRESSED ALL DESIRE TO LET START THE INVESTIGA- TION IN NORTH KOREA THIS IS A JOKE 126
Appendix C: Example of decoded utterances after rescoring (WSJ-Kaldi setup). Rescored with 5-gram model, modified Kneser-Ney smoothed with no count cutoffs (16.60% WER on full Eval 93 set) and RNN LMs (13.11% WER); differences are highlighted by red color, the examples are first sentences in the Eval 93 set that differ after rescoring (not manually chosen): 5-gram: IN TOKYO FOREIGN EXCHANGE TRADING YESTERDAY THE UNIT INCREASED AGAINST THE DOLLAR RNN: IN TOKYO FOREIGN EXCHANGE TRADING YESTERDAY THE YEN IN- CREASED AGAINST THE DOLLAR 5-gram: SOME CURRENCY TRADERS SAID THE UPWARD REVALUATION OF THE GERMAN MARK WASN’T BIG ENOUGH AND THAT THE MARKET MAY CONTINUE TO RISE RNN: SOME CURRENCY TRADERS SAID THE UPWARD REVALUATION OF THE GERMAN MARKET WASN’T BIG ENOUGH AND THAT THE MARKET MAY CONTINUE TO RISE 5-gram: MEANWHILE QUESTIONS REMAIN WITHIN THE E. M. S. WEATH- ERED YESTERDAY’S REALIGNMENT WAS ONLY A TEMPORARY SOLUTION RNN: MEANWHILE QUESTIONS REMAIN WITHIN THE E. M. S. WHETHER YESTERDAY’S REALIGNMENT WAS ONLY A TEMPORARY SOLUTION 5-gram: MR. PARNES FOLEY ALSO FOR THE FIRST TIME THE WIND WITH SUEZ’S PLANS FOR GENERALE DE BELGIQUE’S WAR RNN: MR. PARNES SO LATE ALSO FOR THE FIRST TIME ALIGNED WITH SUEZ’S PLANS FOR GENERALE DE BELGIQUE’S WAR 5-gram: HE SAID THE GROUP WAS MARKET IN ITS STRUCTURE AND NO ONE HAD LEADERSHIP RNN: HE SAID THE GROUP WAS ARCANE IN ITS STRUCTURE AND NO ONE HAD LEADERSHIP 127
Page 1 and 2:
VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
Page 3 and 4:
Abstrakt Statistické jazykové mod
Page 5 and 6:
Contents 1 Introduction 4 1.1 Motiv
Page 7 and 8:
6.2.3 Reduction of Vocabulary Size
Page 9 and 10:
Maybe the most popular vision of fu
Page 11 and 12:
Chapter 6 presents further extensio
Page 13 and 14:
Chapter 2 Overview of Stati
Page 15 and 16:
2.1 Evaluation 2.1.1 Perplexity Eva
Page 17 and 18:
ALP can be used to obtain prior pro
Page 19 and 20:
• Good theoretical motivation •
Page 21 and 22:
abilities of n-grams are stored in
Page 23 and 24:
main (static) n-gram model. As the
Page 25 and 26:
There are many popular examples sho
Page 27 and 28:
y Chen et al., who proposed a so-ca
Page 29 and 30:
confusion among researchers, and ma
Page 31 and 32:
language model took almost a week u
Page 33 and 34:
w(t) s(t-1) s(t) U V W y(t) Figure
Page 35 and 36:
ate is halved at start of every new
Page 37 and 38:
or using matrix-vector notation as
Page 39 and 40:
information for more than 5 time st
Page 41 and 42:
A simple solution to the exploding
Page 43 and 44:
output layer changes to computation
Page 45 and 46:
While RNN models can overcome this
Page 47 and 48:
complex or random architectures (su
Page 49 and 50:
While for any of the previous point
Page 51 and 52:
where λ is the interpolation weigh
Page 53 and 54:
model with default SRILM cutoffs pr
Page 55 and 56:
experiments, we have used the one i
Page 57 and 58:
Perplexity (Penn corpus) 145 140 13
Page 59 and 60:
with syntactical NNLMs would be pre
Page 61 and 62:
Table 4.3: Combination of individua
Page 63 and 64:
Table 4.6: Results on Penn Treebank
Page 65 and 66:
4.6 Conclusion of the Model Combina
Page 67 and 68:
were: 400 classes, hidden layer siz
Page 69 and 70:
Entropy per word on the WSJ test da
Page 71 and 72:
Table 5.3: Results on the WSJ setup
Page 73 and 74:
Table 5.5: Results for models <stro
Page 75 and 76:
trained together with a maximum ent
Page 77 and 78:
wt-3 wt-2 wt-1 D D D P(wt|context)
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 and 128: • compute sentence-level scores g
Page 129: Appendix B: Data generated from mod
Page 133: AND SAYS THIS SWING IS WITHIN A REA
show all

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

Create successful ePaper yourself

Delete template?

Save as template?