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Chapter 6 Strategies for Training Large Scale Neural Network <strong>Language</strong> <strong>Models</strong> The experiments on the Penn Treebank Corpus have shown that mixtures of recurrent neural networks trained by backpropagation through time provide state of the art results in the field of statistical language modeling. However a remaining question is, if the performance would be also this good with much larger amount of the training data - the PTB corpus with about 1M training tokens can be considered as very small, because language models are typically trained on corpora with orders of magnitude more data. It is not unusual to work with huge training corpora that consist of much more than a billion words. While application to low resource domains (especially for new languages and for domains where only small amount of relevant data exists) is also a very interesting research problem, the most convincing results are those obtained with well tuned state of the art systems, which are trained on large amounts of data. The experiments in the previous chapter focused on obtaining the largest possible improvement, however some of the approaches would become computationally difficult to apply to large data sets. In this chapter, we briefly mention existing approaches for reducing the computational complexity of neural net language models (most of these approaches are also applicable to maximum entropy language models). We propose two new simple techniques that can be used to reduce computational complexity of the training and the test phases. We show that these new techniques are complementary to existing approaches. Most interestingly, we show that a standard neural network language model can be 70
trained together with a maximum entropy model, which can be seen as a part of the the neural network, where the input layer is directly connected to the output layer. We intro- duce a hash-<strong>based</strong> implementation of a class-<strong>based</strong> maximum entropy model, that allows us to easily control the trade-off between the memory complexity, the space complexity and the computational complexity. In this chapter, we report results on the NIST RT04 Broadcast News speech recognition task. We use lattices generated from IBM Attila decoder [71] that uses state of the art discriminatively trained acoustic models 1 . The language models for this task are trained on about 400M tokens. This highly competitive setup has been used in the 2010 Speech Recognition with Segmental Conditional Random Fields summer workshop at Johns Hop- kins University 2 [82]. Some of the results reported in this chapter were recently published in [52]. 6.1 Model Description In this section, we will show that a maximum entropy model can be seen as a neural network model with no hidden layer. A maximum entropy model has the following form: P (w|h) = e N i=1 λifi(h,w) w e N i=1 λifi(h,w) , (6.1) where f is a set of features, λ is a set of weights and h is a history. Training of maximum entropy model consists of learning the set of weights λ. Usual features are n-grams, but it is easy to integrate any information source into the model, for example triggers or syntactic features [64]. The choice of features is usually done manually, and significantly affects the overall performance of the model. The standard neural network language model has a very similar form. The main difference is that the features for this model are automatically learned as a function of the history. Also, the usual features for the ME model are binary, while NN models use continuous-valued features. We can describe the NN LM as follows: P (w|h) = e N i=1 λifi(s,w) w e N i=1 λifi(s,w) , (6.2) 1 The lattice rescoring experiments reported in this chapter were performed by Anoop Deoras at JHU due to the license issues of the IBM recognizer. 2 www.clsp.jhu.edu/workshops/archive/ws10/groups/speech-recognition-with-segmental-conditional-random-fields/ 71
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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
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: Table 5.5: Results for models <stro
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
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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
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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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