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[11] T. Brants, A. C. Popat, P. Xu, F. J. Och, and J. Dean. Large language models in machine translation. In Proceedings of the Joint Conference on Empirical Methods in Natural <strong>Language</strong> Processing and Computational <strong>Language</strong> Learning, 2007. [12] L. Burget, P. Schwarz, P. Matějka, M. Hannemann, A. Rastrow, C. White, S. Khu- danpur, H. Heˇrmansk´y, J. Černock´y. Combination of strongly and weakly constrained recognizers for reliable detection of OOVs. In: Proc. ICASSP, 2008. [13] A. Deoras, F. Jelinek. Iterative Decoding: A Novel Re-scoring Framework for Con- fusion Networks. IEEE Automatic Speech Recognition and Understanding (ASRU) Workshop, 2009. [14] A. Deoras, D. Filimonov, M. Harper, F. Jelinek. Model Combination for Speech Recognition using Empirical Bayes Risk Minimization, In: Proceedings of IEEE Workshop on Spoken <strong>Language</strong> Technology (SLT), 2010. [15] A. Deoras, T. Mikolov, S. Kombrink, M. Karafiát, S. Khudanpur. Variational Ap- proximation of Long-Span <strong>Language</strong> <strong>Models</strong> in LVCSR. In Proceedings of ICASSP, 2011. [16] A. Deoras, T. Mikolov, K. Church. A Fast Re-scoring Strategy to Capture Long- Distance Dependencies, In: Proceedings of EMNLP, 2011. [17] J. Elman. Finding Structure in Time. Cognitive Science, 14, 179-211, 1990. [18] J. Elman. Learning and development in neural networks: The importance of starting small. Cognition 48:71.99, 1993. [19] A. Emami, F. Jelinek. Exact training of a neural syntactic language model. In Pro- ceedings of ICASSP, 2004. [20] A. Emami, F. Jelinek. Random clusterings for language modeling. In: Proceedings of ICASSP, 2005. [21] A. Emami. A Neural Syntactic <strong>Language</strong> Model. Ph.D. thesis, Johns Hopkins Uni- versity, 2006. [22] A. Emami, H. J. Kuo, I. Zitouni, L. Mangu. Augmented Context Features for Arabic Speech Recognition. In: Proc. Interspeech, 2010. 114
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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
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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
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 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: Bibliography [1] A. Alexandrescu, K
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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