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Table 7.5: Additional results on the Microsoft Sentence Completion Challenge task. Model Accuracy [%] filtered KN5 47.7 filtered RNNME-100 48.8 RNNME combination 55.4 n-gram models. These results are summarized in Table 7.5, where models trained on modified training data are denoted as filtered. Combination of RNNME models trained on the original and the filtered training data then provides the best result on this task so far, about 55% accuracy. As the task itself is very interesting and shows what language modeling research can focus on in the future, the next chapter will include some of my ideas how good test sets for measuring quality of language models should be created. 7.4 Speech Recognition of Morphologically Rich <strong>Language</strong>s N-gram language models usually work quite well for English, but not so much for other languages. The reason is that for morphologically rich languages, the number of word units is much larger, as new words are formed easily using simple rules, by adding new word ending etc. Having two or more separate sources of information (such as stem and ending) in a single token increases amount of parameters in n-gram models that have to be estimated from the training data. Thus, higher order n-gram models usually do not give much improvement. Other problem is that for these languages, much less training data is usually available. To illustrate the problem, we have used the Penn Treebank Corpus as described in Chapter 4, and added two bits of random information to every token. This should increase perplexity of the model that is trained on these modified data by more than two bits, as it is not possible to revert the process (the information that certain words are similar has to be obtained just from the statistical similarity of occurrence). As the n-gram models cannot perform any clustering, it must be expected that their performance will degrade significantly. On the other hand, RNN models can perform clustering well, thus the increase of entropy should be lower. Results with simple RNN models with the same architecture and KN5 models with no discounts are shown in Table 7.6. 100
Table 7.6: Entropy on PTB with n-gram and RNN models, after adding two bits of random information to every token. Model Entropy Entropy after adding two bits of information KN5 7.14 10.13 RNN 7.19 9.71 Table 7.7: Word accuracy in speech recognition of Czech lectures. Model Word accuracy [%] KN4 70.7 Open vocabulary 4-gram model 71.9 Morphological Random Forest 72.3 NN LMs 75.0 While RNN models were not tuned for the best performance, it can be seen that entropy increased by about 2.5 bits per token, while in case of n-gram models, the increase was about 3 bits. This clearly shows that potential of neural net models to overcome n-gram techniques when modeling inflectional languages is great. In my early work described in [48], I used feedforward neural network language models for a task of speech recognition of Czech lectures. The amount of training data for this task was very low - only about 7M words with less than 1M words of in-domain data. Still, NN LMs did increase accuracy by a large margin over KN4 model. Later experiments performed by Ilya Oparin on this setup did show that also morphological random forest LM can provide improvements on this task [59]; however, less than what was achieved with NN LMs. Results are summarized in Table 7.7 1 . 1 Note that the results in this table are a bit better than those published in [48], as subsequent experiments with larger models did provide further small improvements. 101
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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
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: Table 7.4: Accuracy of different la
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
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