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

More documents

Recommendations

Info

<strong>Statistical</strong> <strong>Language</strong> <strong>Models</strong> Based on Neural Networks Prohláˇsení Prohlaˇsuji, ˇze jsem tuto disertační práci vypracoval samostatně pod vedením Doc. Dr. Ing. Jana Černockého. Uvedl jsem vˇsechny literární publikace, ze kterých jsem čerpal. Některé experimenty byly provedeny ve spolupráci s dalˇsími členy skupiny Speech@FIT, pˇrípadně se studenty z Johns Hopkins University - toto je v práci vˇzdy explicitně uvedeno. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . Tomáˇs Mikolov Květen 2012 I would like to thank my supervisor Jan Černocký for allowing me to explore new approaches to standard problems, for his support and constructive criticism of my work, and for his ability to quickly organize everything related to my studies. I am grateful to Lukáˇs Burget for many advices he gave me about speech recognition systems, for long discussions about many technical details and for his open-minded approach to research. I would also like to thank all members of Speech@FIT group for cooperation, especially Stefan Kombrink, Oldˇrich Plchot, Martin Karafiát, Ondˇrej Glembek and Jiˇrí Kopecký. It was great experience for me to visit Johns Hopkins University during my studies, and I am grateful to Frederick Jelinek and Sanjeev Khudanpur for granting me this opportunity. I always enjoyed discussions with Sanjeev, who was my mentor during my stay there. I also collaborated with other students at JHU, especially Puyang Xu, Scott Novotney and Anoop Deoras. With Anoop, we were able to push state-of-the-art on several standard tasks to new limits, which was the most exciting for me. As my thesis work is <strong>based</strong> on work of Yoshua Bengio, it was great for me that I could have spent several months in his machine learning lab at University of Montreal. I always enjoyed reading Yoshua’s papers, and it was awesome to discuss with him my ideas personally. © Tomáˇs Mikolov, 2012. Tato práce vznikla jako ˇskolní dílo na Vysokém učení technickém v Brně, Fakultě informačních technologií. Práce je chráněna autorským zákonem a její uˇzití bez udělení oprávnění autorem je nezákonné, s výjimkou zákonem definovaných pˇrípad˚u.
Contents 1 Introduction 4 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 Claims of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Overview of <strong>Statistical</strong> <strong>Language</strong> Modeling 9 2.1 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Perplexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.2 Word Error Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 N-gram <strong>Models</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 Advanced <strong>Language</strong> Modeling Techniques . . . . . . . . . . . . . . . . . . . 17 2.3.1 Cache <strong>Language</strong> <strong>Models</strong> . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.2 Class Based <strong>Models</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.3 Structured <strong>Language</strong> <strong>Models</strong> . . . . . . . . . . . . . . . . . . . . . . 20 2.3.4 Decision Trees and Random Forest <strong>Language</strong> <strong>Models</strong> . . . . . . . . . 22 2.3.5 Maximum Entropy <strong>Language</strong> <strong>Models</strong> . . . . . . . . . . . . . . . . . . 22 2.3.6 Neural Network Based <strong>Language</strong> <strong>Models</strong> . . . . . . . . . . . . . . . . 23 2.4 Introduction to Data Sets and Experimental Setups . . . . . . . . . . . . . 24 3 Neural Network <strong>Language</strong> <strong>Models</strong> 26 3.1 Feedforward Neural Network Based <strong>Language</strong> Model . . . . . . . . . . . . . 27 3.2 Recurrent Neural Network Based <strong>Language</strong> Model . . . . . . . . . . . . . . 28 3.3 Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.1 Backpropagation Through Time . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Practical Advices for the Training . . . . . . . . . . . . . . . . . . . 35 3.4 Extensions of NNLMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1
Page 1 and 2: VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
Page 3: Abstrakt Statistické jazykové mod
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 and 130:
Appendix B: Data generated from mod
Page 131 and 132:
Appendix C: Example of decoded utte
Page 133:
AND SAYS THIS SWING IS WITHIN A REA
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?