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VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ BRNO UNIVERSITY OF TECHNOLOGY FAKULTA INFORMAČNÍCH TECHNOLOGIÍ ÚSTAV POČÍTAČOVÉ GRAFIKY A MULTIMÉDIÍ FACULTY OF INFORMATION TECHNOLOGY DEPARTMENT OF COMPUTER GRAPHICS AND MULTIMEDIA STATISTICKÉ JAZYKOVÉ MODELY ZALO ˇZENÉ NA NEURONOV ÝCH SÍTÍCH STATISTICAL LANGUAGE MODELS BASED ON NEURAL NETWORKS DISERTAČNÍ PRÁCE PHD THESIS AUTOR PRÁCE Ing. TOMÁ ˇS MIKOLOV AUTHOR VEDOUCÍ PRÁCE Doc. Dr. Ing. JAN ČERNOCK Ý SUPERVISOR BRNO 2012
Abstrakt Statistické jazykové modely jsou d˚uleˇzitou součástí mnoha úspěˇsných aplikací, mezi něˇz patˇrí napˇríklad automatické rozpoznávání ˇreči a strojový pˇreklad (pˇríkladem je známá aplikace Google Translate). Tradiční techniky pro odhad těchto model˚u jsou zaloˇzeny na tzv. N-gramech. Navzdory známým nedostatk˚um těchto technik a obrovskému úsilí výzkumných skupin napˇríč mnoha oblastmi (rozpoznávání ˇreči, automatický pˇreklad, neuroscience, umělá inteligence, zpracování pˇrirozeného jazyka, komprese dat, psychologie atd.), N-gramy v podstatě z˚ustaly nejúspěˇsnějˇsí technikou. Cílem této práce je prezentace několika architektur jazykových model˚u zaloˇzených na neuronových sítích. Ačkoliv jsou tyto modely výpočetně náročnějˇsí neˇz N-gramové modely, s technikami vyvinutými v této práci je moˇzné jejich efektivní pouˇzití v reálných aplikacích. Dosaˇzené sníˇzení počtu chyb pˇri rozpoznávání ˇreči oproti nejlepˇsím N-gramovým model˚um dosahuje 20%. Model zaloˇzený na rekurentní neurovové síti dosahuje nejlepˇsích publikovaných výsledk˚u na velmi známé datové sadě (Penn Treebank). Abstract <strong>Statistical</strong> language models are crucial part of many successful applications, such as automatic speech recognition and statistical machine translation (for example well-known Google Translate). Traditional techniques for estimating these models are <strong>based</strong> on Ngram counts. Despite known weaknesses of N-grams and huge efforts of research communities across many fields (speech recognition, machine translation, neuroscience, artificial intelligence, natural language processing, data compression, psychology etc.), N-grams remained basically the state-of-the-art. The goal of this thesis is to present various architectures of language models that are <strong>based</strong> on artificial neural networks. Although these models are computationally more expensive than N-gram models, with the presented techniques it is possible to apply them to state-of-the-art systems efficiently. Achieved reductions of word error rate of speech recognition systems are up to 20%, against stateof-the-art N-gram model. The presented recurrent neural network <strong>based</strong> model achieves the best published performance on well-known Penn Treebank setup. Klíčová slova jazykový model, neuronová sít’, rekurentní, maximální entropie, rozpoznávání ˇreči, komprese dat, umělá inteligence Keywords language model, neural network, recurrent, maximum entropy, speech recognition, data compression, artificial intelligence Citace Tomáˇs Mikolov: <strong>Statistical</strong> <strong>Language</strong> <strong>Models</strong> Based on Neural Networks, disertační práce, Brno, FIT VUT v Brně, 2012
Page 1: VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
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
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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
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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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