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

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have to discover while learning a language. The second step would be to design learning algorithms that can learn given patterns using limited amount of resources (number of training examples, computational time). Simplicity of such proposal is actually an advantage: importantly, after the definition of the incrementally more difficult tasks, it should be possible to objectively compare many different approaches and techniques developed by independent researchers. There are of course many possible modifications and extensions of this proposal that may be crucial for successful learning of the language using reasonable amount of resources. For example, it might be important to learn the language together with observing the real world (or simulated world) situations [10]. Also, it might be important to use an active system that does not just passively predict future events. Measuring success of different systems <strong>based</strong> on their ability to predict future events (words) might be sufficient in the beginning, but also can be insufficient in the long run - an AI system should probably aim to maximize some reward function. The representation of the AI system itself is another important topic: due to computational limitations, traditional neural network architectures might be insufficient. Repre- senting AI by a Turing machine or a computer program that is automatically constructed during training can be a better option, although with other difficulties as discussed in [43]. Clearly, the deeper we go, the more questions arise - thus, my opinion is that the prob- lem of language understanding by computers should be solved step by step, starting with simple problems where various techniques that can be easily compared. 108
Chapter 9 Conclusion and Future Work I hope this work will help to make the statistical language modeling more attractive for future research. The achieved results clearly show that n-gram models can no longer be considered as state of the art, and that significant improvements in many applications can be obtained by incorporating neural network <strong>based</strong> language models. Moreover, I tried to show links between the language modeling, machine learning, natural language processing and artificial intelligence. The main conclusions of this work are: • Recurrent neural network language models can be successfully trained by using stochastic gradient descent and backpropagation through time • Great speedup can be achieved by using simple classes in the output layer; main advantage over other similar proposed techniques is simplicity • Comparison and combination of many advanced techniques shows that RNN language models reach state-of-the-art performance on several setups, mainly the well- known Penn Treebank Corpus • With increasing amount of the training data, the potential for improvements with RNN LMs over n-gram models is increasing; however, it is crucial to also increase number of parameters in the model (mainly the hidden layer size) • If hidden layer size is kept constant, the potential improvements from neural network language models is decreasing with more training data • RNN LMs can be easily trained together with maximum entropy models (the RN- NME architecture); this seems especially useful for very large data sets - RNNME 109
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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
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main (static) n-gram model. As the
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There are many popular examples sho
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y Chen et al., who proposed a so-ca
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confusion among researchers, and ma
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language model took almost a week u
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w(t) s(t-1) s(t) U V W y(t) Figure
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ate is halved at start of every new
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or using matrix-vector notation as
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information for more than 5 time st
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A simple solution to the exploding
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output layer changes to computation
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While RNN models can overcome this
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complex or random architectures (su
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While for any of the previous point
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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
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: supervision such as one digit at a
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
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