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<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
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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
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Table 6.1: Training corpora for NIS
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Perplexity 360 340 320 300 280 260
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Entropy per word 9 8.5 8 7.5 7 6.5
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1 a a a 1 2 3 P(w(t)|*) ONE TWO THR
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Table 6.4: Perplexity on the evalua
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Entropy reduction per word over KN4
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Table 6.6: Perplexity with the new
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Entropy reduction over KN5 -0.04 -0
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as a baseline, and 12.3% after resc
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Table 7.1: BLEU on IWSLT 2005 Machi
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Table 7.3: Size of compressed text
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Table 7.4: Accuracy of different la
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Table 7.6: Entropy on PTB with n-gr
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8.1 Machine Learning One possible d
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that almost every non-trivial compu
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supervision such as one digit at a
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Chapter 9 Conclusion and Future Wor
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from the expensive part of the mode
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Bibliography [1] A. Alexandrescu, K
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[23] D. Filimonov, M. Harper. A joi
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[50] T. Mikolov, S. Kombrink, L. Bu
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[77] W. Wang, M. Harper. The SuperA
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Test Phase After the model is train
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• compute sentence-level scores g
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Appendix B: Data generated from mod
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Appendix C: Example of decoded utte
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AND SAYS THIS SWING IS WITHIN A REA
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